def __init__(
self,
manifest_filepath: str,
parser: Union[str, Callable],
sample_rate: int,
int_values: bool = False,
augmentor: 'nemo.collections.asr.parts.perturb.AudioAugmentor' = None,
max_duration: Optional[int] = None,
min_duration: Optional[int] = None,
max_utts: int = 0,
trim: bool = False,
bos_id: Optional[int] = None,
eos_id: Optional[int] = None,
pad_id: int = 0,
return_sample_id: bool = False,
):
if type(manifest_filepath) == str:
manifest_filepath = manifest_filepath.split(",")
self.manifest_processor = ASRManifestProcessor(
manifest_filepath=manifest_filepath,
parser=parser,
max_duration=max_duration,
min_duration=min_duration,
max_utts=max_utts,
bos_id=bos_id,
eos_id=eos_id,
pad_id=pad_id,
)
self.featurizer = WaveformFeaturizer(sample_rate=sample_rate,
int_values=int_values,
augmentor=augmentor)
self.trim = trim
self.return_sample_id = return_sample_id
def get_batch_embeddings(speaker_model, manifest_filepath, batch_size=32, sample_rate=16000, device='cuda'):
speaker_model.eval()
if device == 'cuda':
speaker_model.to(device)
featurizer = WaveformFeaturizer(sample_rate=sample_rate)
dataset = AudioToSpeechLabelDataset(manifest_filepath=manifest_filepath, labels=None, featurizer=featurizer)
dataloader = torch.utils.data.DataLoader(
dataset=dataset, batch_size=batch_size, collate_fn=dataset.fixed_seq_collate_fn,
)
all_logits = []
all_labels = []
all_embs = []
for test_batch in tqdm(dataloader):
if device == 'cuda':
test_batch = [x.to(device) for x in test_batch]
audio_signal, audio_signal_len, labels, _ = test_batch
logits, embs = speaker_model.forward(input_signal=audio_signal, input_signal_length=audio_signal_len)
all_logits.extend(logits.cpu().numpy())
all_labels.extend(labels.cpu().numpy())
all_embs.extend(embs.cpu().numpy())
all_logits, true_labels, all_embs = np.asarray(all_logits), np.asarray(all_labels), np.asarray(all_embs)
return all_embs, all_logits, true_labels, dataset.id2label
def __setup_dataloader_from_config(self, config: Optional[Dict]):
if 'augmentor' in config:
augmentor = process_augmentations(config['augmentor'])
else:
augmentor = None
featurizer = WaveformFeaturizer(
sample_rate=config['sample_rate'], int_values=config.get('int_values', False), augmentor=augmentor
)
shuffle = config.get('shuffle', False)
if config.get('is_tarred', False):
if ('tarred_audio_filepaths' in config and config['tarred_audio_filepaths'] is None) or (
'manifest_filepath' in config and config['manifest_filepath'] is None
):
logging.warning(
"Could not load dataset as `manifest_filepath` was None or "
f"`tarred_audio_filepaths` is None. Provided config : {config}"
)
return None
shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0
dataset = get_tarred_speech_label_dataset(
featurizer=featurizer,
config=config,
shuffle_n=shuffle_n,
global_rank=self.global_rank,
world_size=self.world_size,
)
shuffle = False
else:
if 'manifest_filepath' in config and config['manifest_filepath'] is None:
logging.warning(f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}")
return None
dataset = AudioToSpeechLabelDataset(
manifest_filepath=config['manifest_filepath'],
labels=config['labels'],
featurizer=featurizer,
max_duration=config.get('max_duration', None),
min_duration=config.get('min_duration', None),
trim=config.get('trim_silence', False),
normalize_audio=config.get('normalize_audio', False),
)
if hasattr(dataset, 'fixed_seq_collate_fn'):
collate_fn = dataset.fixed_seq_collate_fn
else:
collate_fn = dataset.datasets[0].fixed_seq_collate_fn
batch_size = config['batch_size']
return torch.utils.data.DataLoader(
dataset=dataset,
batch_size=batch_size,
collate_fn=collate_fn,
drop_last=config.get('drop_last', False),
shuffle=shuffle,
num_workers=config.get('num_workers', 0),
pin_memory=config.get('pin_memory', False),
)
def __setup_dataloader_from_config(self, config: Optional[Dict]):
if 'augmentor' in config:
augmentor = process_augmentations(config['augmentor'])
else:
augmentor = None
featurizer = WaveformFeaturizer(sample_rate=config['sample_rate'],
int_values=config.get(
'int_values', False),
augmentor=augmentor)
self.dataset = AudioToSpeechLabelDataset(
manifest_filepath=config['manifest_filepath'],
labels=config['labels'],
featurizer=featurizer,
max_duration=config.get('max_duration', None),
min_duration=config.get('min_duration', None),
trim=False,
time_length=config.get('time_length', 8),
shift_length=config.get('shift_length', 0.75),
)
if self.task == 'diarization':
logging.info("Setting up diarization parameters")
_collate_func = self.dataset.sliced_seq_collate_fn
batch_size = config['batch_size']
shuffle = False
else:
logging.info("Setting up identification parameters")
_collate_func = self.dataset.fixed_seq_collate_fn
batch_size = config['batch_size']
shuffle = config.get('shuffle', False)
return torch.utils.data.DataLoader(
dataset=self.dataset,
batch_size=batch_size,
collate_fn=_collate_func,
drop_last=config.get('drop_last', False),
shuffle=shuffle,
num_workers=config.get('num_workers', 0),
pin_memory=config.get('pin_memory', False),
)
def test_tarred_dataset_duplicate_name(self, test_data_dir):
manifest_path = os.path.abspath(
os.path.join(
test_data_dir,
'asr/tarred_an4/tarred_duplicate_audio_manifest.json'))
# Test braceexpand loading
tarpath = os.path.abspath(
os.path.join(test_data_dir, 'asr/tarred_an4/audio_{0..1}.tar'))
featurizer = WaveformFeaturizer(sample_rate=16000,
int_values=False,
augmentor=None)
ds_braceexpand = TarredAudioToClassificationLabelDataset(
audio_tar_filepaths=tarpath,
manifest_filepath=manifest_path,
labels=self.labels,
featurizer=featurizer)
assert len(ds_braceexpand) == 6
count = 0
for _ in ds_braceexpand:
count += 1
assert count == 6
# Test loading via list
tarpath = [
os.path.abspath(
os.path.join(test_data_dir, f'asr/tarred_an4/audio_{i}.tar'))
for i in range(2)
]
ds_list_load = TarredAudioToClassificationLabelDataset(
audio_tar_filepaths=tarpath,
manifest_filepath=manifest_path,
labels=self.labels,
featurizer=featurizer)
count = 0
for _ in ds_list_load:
count += 1
assert count == 6
class VocoderDataset(Dataset):
def __init__(
self,
manifest_filepath: Union[str, Path, List[str], List[Path]],
sample_rate: int,
n_segments: Optional[int] = None,
max_duration: Optional[float] = None,
min_duration: Optional[float] = None,
ignore_file: Optional[Union[str, Path]] = None,
trim: Optional[bool] = False,
load_precomputed_mel: bool = False,
hop_length: Optional[int] = None,
):
"""Dataset which can be used for training and fine-tuning vocoder with pre-computed mel-spectrograms.
Args:
manifest_filepath (Union[str, Path, List[str], List[Path]]): Path(s) to the .json manifests containing information on the
dataset. Each line in the .json file should be valid json. Note: the .json file itself is not valid
json. Each line should contain the following:
"audio_filepath": <PATH_TO_WAV>,
"duration": <Duration of audio clip in seconds> (Optional),
"mel_filepath": <PATH_TO_LOG_MEL_PT> (Optional)
sample_rate (int): The sample rate of the audio. Or the sample rate that we will resample all files to.
n_segments (int): The length of audio in samples to load. For example, given a sample rate of 16kHz, and
n_segments=16000, a random 1 second section of audio from the clip will be loaded. The section will
be randomly sampled everytime the audio is batched. Can be set to None to load the entire audio.
Must be specified if load_precomputed_mel is True.
max_duration (Optional[float]): Max duration of audio clips in seconds. All samples exceeding this will be
pruned prior to training. Note: Requires "duration" to be set in the manifest file. It does not load
audio to compute duration. Defaults to None which does not prune.
min_duration (Optional[float]): Min duration of audio clips in seconds. All samples lower than this will be
pruned prior to training. Note: Requires "duration" to be set in the manifest file. It does not load
audio to compute duration. Defaults to None which does not prune.
ignore_file (Optional[Union[str, Path]]): The location of a pickle-saved list of audio paths
that will be pruned prior to training. Defaults to None which does not prune.
trim (bool): Whether to apply librosa.effects.trim to the audio file. Defaults to False.
load_precomputed_mel (bool): Whether to load precomputed mel (useful for fine-tuning). Note: Requires "mel_filepath" to be set in the manifest file.
hop_length (Optional[int]): The hope length between fft computations. Must be specified if load_precomputed_mel is True.
"""
super().__init__()
if load_precomputed_mel:
if hop_length is None:
raise ValueError(
"hop_length must be specified when load_precomputed_mel is True"
)
if n_segments is None:
raise ValueError(
"n_segments must be specified when load_precomputed_mel is True"
)
# Initialize and read manifest file(s), filter out data by duration and ignore_file
if isinstance(manifest_filepath, str):
manifest_filepath = [manifest_filepath]
self.manifest_filepath = manifest_filepath
data = []
total_duration = 0
for manifest_file in self.manifest_filepath:
with open(Path(manifest_file).expanduser(), 'r') as f:
logging.info(f"Loading dataset from {manifest_file}.")
for line in tqdm(f):
item = json.loads(line)
if "mel_filepath" not in item and load_precomputed_mel:
raise ValueError(
f"mel_filepath is missing in {manifest_file}")
file_info = {
"audio_filepath":
item["audio_filepath"],
"mel_filepath":
item["mel_filepath"]
if "mel_filepath" in item else None,
"duration":
item["duration"] if "duration" in item else None,
}
data.append(file_info)
if file_info["duration"] is None:
logging.info(
"Not all audio files have duration information. Duration logging will be disabled."
)
total_duration = None
if total_duration is not None:
total_duration += item["duration"]
logging.info(f"Loaded dataset with {len(data)} files.")
if total_duration is not None:
logging.info(
f"Dataset contains {total_duration / 3600:.2f} hours.")
self.data = TTSDataset.filter_files(data, ignore_file, min_duration,
max_duration, total_duration)
self.base_data_dir = get_base_dir(
[item["audio_filepath"] for item in self.data])
# Initialize audio and mel related parameters
self.load_precomputed_mel = load_precomputed_mel
self.featurizer = WaveformFeaturizer(sample_rate=sample_rate)
self.sample_rate = sample_rate
self.n_segments = n_segments
self.hop_length = hop_length
self.trim = trim
def _collate_fn(self, batch):
if self.load_precomputed_mel:
return torch.utils.data.dataloader.default_collate(batch)
audio_lengths = [audio_len for _, audio_len in batch]
audio_signal = torch.zeros(len(batch),
max(audio_lengths),
dtype=torch.float)
for i, sample in enumerate(batch):
audio_signal[i].narrow(0, 0, sample[0].size(0)).copy_(sample[0])
return audio_signal, torch.tensor(audio_lengths, dtype=torch.long)
def __getitem__(self, index):
sample = self.data[index]
if not self.load_precomputed_mel:
features = AudioSegment.segment_from_file(
sample["audio_filepath"],
n_segments=self.n_segments
if self.n_segments is not None else -1,
trim=self.trim,
)
features = torch.tensor(features.samples)
audio, audio_length = features, torch.tensor(
features.shape[0]).long()
return audio, audio_length
else:
features = self.featurizer.process(sample["audio_filepath"],
trim=self.trim)
audio, audio_length = features, torch.tensor(
features.shape[0]).long()
mel = torch.load(sample["mel_filepath"])
frames = math.ceil(self.n_segments / self.hop_length)
if len(audio) > self.n_segments:
start = random.randint(0, mel.shape[1] - frames - 2)
mel = mel[:, start:start + frames]
audio = audio[start * self.hop_length:(start + frames) *
self.hop_length]
else:
mel = torch.nn.functional.pad(mel, (0, frames - mel.shape[1]))
audio = torch.nn.functional.pad(
audio, (0, self.n_segments - len(audio)))
return audio, len(audio), mel
def __len__(self):
return len(self.data)
def __init__(
self,
manifest_filepath: Union[str, Path, List[str], List[Path]],
sample_rate: int,
n_segments: Optional[int] = None,
max_duration: Optional[float] = None,
min_duration: Optional[float] = None,
ignore_file: Optional[Union[str, Path]] = None,
trim: Optional[bool] = False,
load_precomputed_mel: bool = False,
hop_length: Optional[int] = None,
):
"""Dataset which can be used for training and fine-tuning vocoder with pre-computed mel-spectrograms.
Args:
manifest_filepath (Union[str, Path, List[str], List[Path]]): Path(s) to the .json manifests containing information on the
dataset. Each line in the .json file should be valid json. Note: the .json file itself is not valid
json. Each line should contain the following:
"audio_filepath": <PATH_TO_WAV>,
"duration": <Duration of audio clip in seconds> (Optional),
"mel_filepath": <PATH_TO_LOG_MEL_PT> (Optional)
sample_rate (int): The sample rate of the audio. Or the sample rate that we will resample all files to.
n_segments (int): The length of audio in samples to load. For example, given a sample rate of 16kHz, and
n_segments=16000, a random 1 second section of audio from the clip will be loaded. The section will
be randomly sampled everytime the audio is batched. Can be set to None to load the entire audio.
Must be specified if load_precomputed_mel is True.
max_duration (Optional[float]): Max duration of audio clips in seconds. All samples exceeding this will be
pruned prior to training. Note: Requires "duration" to be set in the manifest file. It does not load
audio to compute duration. Defaults to None which does not prune.
min_duration (Optional[float]): Min duration of audio clips in seconds. All samples lower than this will be
pruned prior to training. Note: Requires "duration" to be set in the manifest file. It does not load
audio to compute duration. Defaults to None which does not prune.
ignore_file (Optional[Union[str, Path]]): The location of a pickle-saved list of audio paths
that will be pruned prior to training. Defaults to None which does not prune.
trim (bool): Whether to apply librosa.effects.trim to the audio file. Defaults to False.
load_precomputed_mel (bool): Whether to load precomputed mel (useful for fine-tuning). Note: Requires "mel_filepath" to be set in the manifest file.
hop_length (Optional[int]): The hope length between fft computations. Must be specified if load_precomputed_mel is True.
"""
super().__init__()
if load_precomputed_mel:
if hop_length is None:
raise ValueError(
"hop_length must be specified when load_precomputed_mel is True"
)
if n_segments is None:
raise ValueError(
"n_segments must be specified when load_precomputed_mel is True"
)
# Initialize and read manifest file(s), filter out data by duration and ignore_file
if isinstance(manifest_filepath, str):
manifest_filepath = [manifest_filepath]
self.manifest_filepath = manifest_filepath
data = []
total_duration = 0
for manifest_file in self.manifest_filepath:
with open(Path(manifest_file).expanduser(), 'r') as f:
logging.info(f"Loading dataset from {manifest_file}.")
for line in tqdm(f):
item = json.loads(line)
if "mel_filepath" not in item and load_precomputed_mel:
raise ValueError(
f"mel_filepath is missing in {manifest_file}")
file_info = {
"audio_filepath":
item["audio_filepath"],
"mel_filepath":
item["mel_filepath"]
if "mel_filepath" in item else None,
"duration":
item["duration"] if "duration" in item else None,
}
data.append(file_info)
if file_info["duration"] is None:
logging.info(
"Not all audio files have duration information. Duration logging will be disabled."
)
total_duration = None
if total_duration is not None:
total_duration += item["duration"]
logging.info(f"Loaded dataset with {len(data)} files.")
if total_duration is not None:
logging.info(
f"Dataset contains {total_duration / 3600:.2f} hours.")
self.data = TTSDataset.filter_files(data, ignore_file, min_duration,
max_duration, total_duration)
self.base_data_dir = get_base_dir(
[item["audio_filepath"] for item in self.data])
# Initialize audio and mel related parameters
self.load_precomputed_mel = load_precomputed_mel
self.featurizer = WaveformFeaturizer(sample_rate=sample_rate)
self.sample_rate = sample_rate
self.n_segments = n_segments
self.hop_length = hop_length
self.trim = trim
class TTSDataset(Dataset):
def __init__(
self,
manifest_filepath: Union[str, Path, List[str], List[Path]],
sample_rate: int,
text_tokenizer: Union[BaseTokenizer, Callable[[str], List[int]]],
tokens: Optional[List[str]] = None,
text_normalizer: Optional[Union[Normalizer, Callable[[str],
str]]] = None,
text_normalizer_call_kwargs: Optional[Dict] = None,
text_tokenizer_pad_id: Optional[int] = None,
sup_data_types: Optional[List[str]] = None,
sup_data_path: Optional[Union[Path, str]] = None,
max_duration: Optional[float] = None,
min_duration: Optional[float] = None,
ignore_file: Optional[Union[str, Path]] = None,
trim: bool = False,
n_fft: int = 1024,
win_length: Optional[int] = None,
hop_length: Optional[int] = None,
window: str = "hann",
n_mels: int = 80,
lowfreq: int = 0,
highfreq: Optional[int] = None,
**kwargs,
):
"""Dataset which can be used for training spectrogram generators and end-to-end TTS models.
It loads main data types (audio, text) and specified supplementary data types (log mel, durations, align prior matrix, pitch, energy, speaker id).
Some of supplementary data types will be computed on the fly and saved in the sup_data_path if they did not exist before.
Saved folder can be changed for some supplementary data types (see keyword args section).
Arguments for supplementary data should be also specified in this class and they will be used from kwargs (see keyword args section).
Args:
manifest_filepath (Union[str, Path, List[str], List[Path]]): Path(s) to the .json manifests containing information on the
dataset. Each line in the .json file should be valid json. Note: the .json file itself is not valid
json. Each line should contain the following:
"audio_filepath": <PATH_TO_WAV>,
"text": <THE_TRANSCRIPT>,
"normalized_text": <NORMALIZED_TRANSCRIPT> (Optional),
"mel_filepath": <PATH_TO_LOG_MEL_PT> (Optional),
"duration": <Duration of audio clip in seconds> (Optional)
sample_rate (int): The sample rate of the audio. Or the sample rate that we will resample all files to.
text_tokenizer (Optional[Union[BaseTokenizer, Callable[[str], List[int]]]]): BaseTokenizer or callable which represents text tokenizer.
tokens (Optional[List[str]]): Tokens from text_tokenizer. Should be specified if text_tokenizer is not BaseTokenizer.
text_normalizer (Optional[Union[Normalizer, Callable[[str], str]]]): Normalizer or callable which represents text normalizer.
text_normalizer_call_kwargs (Optional[Dict]): Additional arguments for text_normalizer function.
text_tokenizer_pad_id (Optional[int]): Index of padding. Should be specified if text_tokenizer is not BaseTokenizer.
sup_data_types (Optional[List[str]]): List of supplementary data types.
sup_data_path (Optional[Union[Path, str]]): A folder that contains or will contain supplementary data (e.g. pitch).
max_duration (Optional[float]): Max duration of audio clips in seconds. All samples exceeding this will be
pruned prior to training. Note: Requires "duration" to be set in the manifest file. It does not load
audio to compute duration. Defaults to None which does not prune.
min_duration (Optional[float]): Min duration of audio clips in seconds. All samples lower than this will be
pruned prior to training. Note: Requires "duration" to be set in the manifest file. It does not load
audio to compute duration. Defaults to None which does not prune.
ignore_file (Optional[Union[str, Path]]): The location of a pickle-saved list of audio paths
that will be pruned prior to training. Defaults to None which does not prune.
trim (Optional[bool]): Whether to apply librosa.effects.trim to the audio file. Defaults to False.
n_fft (int): The number of fft samples. Defaults to 1024
win_length (Optional[int]): The length of the stft windows. Defaults to None which uses n_fft.
hop_length (Optional[int]): The hope length between fft computations. Defaults to None which uses n_fft//4.
window (str): One of 'hann', 'hamming', 'blackman','bartlett', 'none'. Which corresponds to the
equivalent torch window function.
n_mels (int): The number of mel filters. Defaults to 80.
lowfreq (int): The lowfreq input to the mel filter calculation. Defaults to 0.
highfreq (Optional[int]): The highfreq input to the mel filter calculation. Defaults to None.
Keyword Args:
log_mel_folder (Optional[Union[Path, str]]): The folder that contains or will contain log mel spectrograms.
align_prior_matrix_folder (Optional[Union[Path, str]]): The folder that contains or will contain align prior matrices.
pitch_folder (Optional[Union[Path, str]]): The folder that contains or will contain pitch.
energy_folder (Optional[Union[Path, str]]): The folder that contains or will contain energy.
durs_file (Optional[str]): String path to pickled durations location.
durs_type (Optional[str]): Type of durations. Currently supported only "aligner-based".
use_beta_binomial_interpolator (Optional[bool]): Whether to use beta-binomial interpolator for calculating alignment prior matrix. Defaults to False.
pitch_fmin (Optional[float]): The fmin input to librosa.pyin. Defaults to librosa.note_to_hz('C2').
pitch_fmax (Optional[float]): The fmax input to librosa.pyin. Defaults to librosa.note_to_hz('C7').
pitch_mean (Optional[float]): The mean that we use to normalize the pitch.
pitch_std (Optional[float]): The std that we use to normalize the pitch.
pitch_norm (Optional[bool]): Whether to normalize pitch (via pitch_mean and pitch_std) or not.
"""
super().__init__()
# Initialize text tokenizer
self.text_tokenizer = text_tokenizer
if isinstance(self.text_tokenizer, BaseTokenizer):
self.text_tokenizer_pad_id = text_tokenizer.pad
self.tokens = text_tokenizer.tokens
else:
if text_tokenizer_pad_id is None:
raise ValueError(
f"text_tokenizer_pad_id must be specified if text_tokenizer is not BaseTokenizer"
)
if tokens is None:
raise ValueError(
f"tokens must be specified if text_tokenizer is not BaseTokenizer"
)
self.text_tokenizer_pad_id = text_tokenizer_pad_id
self.tokens = tokens
# Initialize text normalizer is specified
self.text_normalizer = text_normalizer
self.text_normalizer_call = (
self.text_normalizer.normalize if isinstance(
self.text_normalizer, Normalizer) else self.text_normalizer)
self.text_normalizer_call_kwargs = (text_normalizer_call_kwargs
if text_normalizer_call_kwargs
is not None else {})
# Initialize and read manifest file(s), filter out data by duration and ignore_file, compute base dir
if isinstance(manifest_filepath, str):
manifest_filepath = [manifest_filepath]
self.manifest_filepath = manifest_filepath
data = []
total_duration = 0
for manifest_file in self.manifest_filepath:
with open(Path(manifest_file).expanduser(), 'r') as f:
logging.info(f"Loading dataset from {manifest_file}.")
for line in tqdm(f):
item = json.loads(line)
file_info = {
"audio_filepath":
item["audio_filepath"],
"original_text":
item["text"],
"mel_filepath":
item["mel_filepath"]
if "mel_filepath" in item else None,
"duration":
item["duration"] if "duration" in item else None,
"speaker_id":
item["speaker"] if "speaker" in item else None,
}
if "normalized_text" not in item:
text = item["text"]
if self.text_normalizer is not None:
text = self.text_normalizer_call(
text, **self.text_normalizer_call_kwargs)
file_info["normalized_text"] = text
file_info["text_tokens"] = self.text_tokenizer(text)
else:
file_info["normalized_text"] = item["normalized_text"]
file_info["text_tokens"] = self.text_tokenizer(
item["normalized_text"])
data.append(file_info)
if file_info["duration"] is None:
logging.info(
"Not all audio files have duration information. Duration logging will be disabled."
)
total_duration = None
if total_duration is not None:
total_duration += item["duration"]
logging.info(f"Loaded dataset with {len(data)} files.")
if total_duration is not None:
logging.info(
f"Dataset contains {total_duration / 3600:.2f} hours.")
self.data = TTSDataset.filter_files(data, ignore_file, min_duration,
max_duration, total_duration)
self.base_data_dir = get_base_dir(
[item["audio_filepath"] for item in self.data])
# Initialize audio and mel related parameters
self.sample_rate = sample_rate
self.featurizer = WaveformFeaturizer(sample_rate=self.sample_rate)
self.trim = trim
self.n_fft = n_fft
self.n_mels = n_mels
self.lowfreq = lowfreq
self.highfreq = highfreq
self.window = window
self.win_length = win_length or self.n_fft
self.hop_length = hop_length
self.hop_len = self.hop_length or self.n_fft // 4
self.fb = torch.tensor(
librosa.filters.mel(self.sample_rate,
self.n_fft,
n_mels=self.n_mels,
fmin=self.lowfreq,
fmax=self.highfreq),
dtype=torch.float,
).unsqueeze(0)
window_fn = {
'hann': torch.hann_window,
'hamming': torch.hamming_window,
'blackman': torch.blackman_window,
'bartlett': torch.bartlett_window,
'none': None,
}.get(self.window, None)
self.stft = lambda x: torch.stft(
input=x,
n_fft=self.n_fft,
hop_length=self.hop_len,
win_length=self.win_length,
window=window_fn(self.win_length, periodic=False).to(torch.float)
if window_fn else None,
)
# Initialize sup_data_path, sup_data_types and run preprocessing methods for every supplementary data type
if sup_data_path is not None:
Path(sup_data_path).mkdir(parents=True, exist_ok=True)
self.sup_data_path = sup_data_path
self.sup_data_types = ([
DATA_STR2DATA_CLASS[d_as_str] for d_as_str in sup_data_types
] if sup_data_types is not None else [])
self.sup_data_types_set = set(self.sup_data_types)
for data_type in self.sup_data_types:
if data_type not in VALID_SUPPLEMENTARY_DATA_TYPES:
raise NotImplementedError(
f"Current implementation doesn't support {data_type} type."
)
getattr(self, f"add_{data_type.name}")(**kwargs)
@staticmethod
def filter_files(data, ignore_file, min_duration, max_duration,
total_duration):
if ignore_file:
logging.info(f"Using {ignore_file} to prune dataset.")
with open(Path(ignore_file).expanduser(), "rb") as f:
wavs_to_ignore = set(pickle.load(f))
filtered_data: List[Dict] = []
pruned_duration = 0 if total_duration is not None else None
pruned_items = 0
for item in data:
audio_path = item['audio_filepath']
# Prune data according to min/max_duration & the ignore file
if total_duration is not None:
if (min_duration and item["duration"] < min_duration) or (
max_duration and item["duration"] > max_duration):
pruned_duration += item["duration"]
pruned_items += 1
continue
if ignore_file and (audio_path in wavs_to_ignore):
pruned_items += 1
pruned_duration += item["duration"]
wavs_to_ignore.remove(audio_path)
continue
filtered_data.append(item)
logging.info(
f"Pruned {pruned_items} files. Final dataset contains {len(filtered_data)} files"
)
if pruned_duration is not None:
logging.info(
f"Pruned {pruned_duration / 3600:.2f} hours. Final dataset contains "
f"{(total_duration - pruned_duration) / 3600:.2f} hours.")
return filtered_data
def add_log_mel(self, **kwargs):
self.log_mel_folder = kwargs.pop('log_mel_folder', None)
if self.log_mel_folder is None:
self.log_mel_folder = Path(self.sup_data_path) / LogMel.name
self.log_mel_folder.mkdir(exist_ok=True, parents=True)
def add_durations(self, **kwargs):
durs_file = kwargs.pop('durs_file')
durs_type = kwargs.pop('durs_type')
audio_stem2durs = torch.load(durs_file)
self.durs = []
for tag in [Path(d["audio_filepath"]).stem for d in self.data]:
durs = audio_stem2durs[tag]
if durs_type == "aligner-based":
self.durs.append(durs)
else:
raise NotImplementedError(
f"{durs_type} duration type is not supported. Only aligner-based is supported at this moment."
)
def add_align_prior_matrix(self, **kwargs):
self.align_prior_matrix_folder = kwargs.pop(
'align_prior_matrix_folder', None)
if self.align_prior_matrix_folder is None:
self.align_prior_matrix_folder = Path(
self.sup_data_path) / AlignPriorMatrix.name
self.align_prior_matrix_folder.mkdir(exist_ok=True, parents=True)
self.use_beta_binomial_interpolator = kwargs.pop(
'use_beta_binomial_interpolator', False)
if self.use_beta_binomial_interpolator:
self.beta_binomial_interpolator = BetaBinomialInterpolator()
def add_pitch(self, **kwargs):
self.pitch_folder = kwargs.pop('pitch_folder', None)
if self.pitch_folder is None:
self.pitch_folder = Path(self.sup_data_path) / Pitch.name
self.pitch_folder.mkdir(exist_ok=True, parents=True)
self.pitch_fmin = kwargs.pop("pitch_fmin", librosa.note_to_hz('C2'))
self.pitch_fmax = kwargs.pop("pitch_fmax", librosa.note_to_hz('C7'))
self.pitch_mean = kwargs.pop("pitch_mean", None)
self.pitch_std = kwargs.pop("pitch_std", None)
self.pitch_norm = kwargs.pop("pitch_norm", False)
def add_energy(self, **kwargs):
self.energy_folder = kwargs.pop('energy_folder', None)
if self.energy_folder is None:
self.energy_folder = Path(self.sup_data_path) / Energy.name
self.energy_folder.mkdir(exist_ok=True, parents=True)
def add_speaker_id(self, **kwargs):
pass
def get_spec(self, audio):
with torch.cuda.amp.autocast(enabled=False):
spec = self.stft(audio)
if spec.dtype in [torch.cfloat, torch.cdouble]:
spec = torch.view_as_real(spec)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-9)
return spec
def get_log_mel(self, audio):
with torch.cuda.amp.autocast(enabled=False):
spec = self.get_spec(audio)
mel = torch.matmul(self.fb.to(spec.dtype), spec)
log_mel = torch.log(
torch.clamp(mel, min=torch.finfo(mel.dtype).tiny))
return log_mel
def __getitem__(self, index):
sample = self.data[index]
# Let's keep audio name and all internal directories in rel_audio_path_as_text_id to avoid any collisions
rel_audio_path = Path(sample["audio_filepath"]).relative_to(
self.base_data_dir).with_suffix("")
rel_audio_path_as_text_id = str(rel_audio_path).replace("/", "_")
# Load audio
features = self.featurizer.process(sample["audio_filepath"],
trim=self.trim)
audio, audio_length = features, torch.tensor(features.shape[0]).long()
# Load text
text = torch.tensor(sample["text_tokens"]).long()
text_length = torch.tensor(len(sample["text_tokens"])).long()
# Load mel if needed
log_mel, log_mel_length = None, None
if LogMel in self.sup_data_types_set:
mel_path = sample["mel_filepath"]
if mel_path is not None and Path(mel_path).exists():
log_mel = torch.load(mel_path)
else:
mel_path = self.log_mel_folder / f"{rel_audio_path_as_text_id}.pt"
if mel_path.exists():
log_mel = torch.load(mel_path)
else:
log_mel = self.get_log_mel(audio)
torch.save(log_mel, mel_path)
log_mel = log_mel.squeeze(0)
log_mel_length = torch.tensor(log_mel.shape[1]).long()
# Load durations if needed
durations = None
if Durations in self.sup_data_types_set:
durations = self.durs[index]
# Load alignment prior matrix if needed
align_prior_matrix = None
if AlignPriorMatrix in self.sup_data_types_set:
if self.use_beta_binomial_interpolator:
mel_len = self.get_log_mel(audio).shape[2]
align_prior_matrix = torch.from_numpy(
self.beta_binomial_interpolator(mel_len,
text_length.item()))
else:
prior_path = self.align_prior_matrix_folder / f"{rel_audio_path_as_text_id}.pt"
if prior_path.exists():
align_prior_matrix = torch.load(prior_path)
else:
mel_len = self.get_log_mel(audio).shape[2]
align_prior_matrix = beta_binomial_prior_distribution(
text_length, mel_len)
align_prior_matrix = torch.from_numpy(align_prior_matrix)
torch.save(align_prior_matrix, prior_path)
# Load pitch if needed
pitch, pitch_length = None, None
if Pitch in self.sup_data_types_set:
pitch_path = self.pitch_folder / f"{rel_audio_path_as_text_id}.pt"
if pitch_path.exists():
pitch = torch.load(pitch_path).float()
else:
pitch, _, _ = librosa.pyin(
audio.numpy(),
fmin=self.pitch_fmin,
fmax=self.pitch_fmax,
frame_length=self.win_length,
sr=self.sample_rate,
fill_na=0.0,
)
pitch = torch.from_numpy(pitch).float()
torch.save(pitch, pitch_path)
if self.pitch_mean is not None and self.pitch_std is not None and self.pitch_norm:
pitch -= self.pitch_mean
pitch[
pitch == -self.
pitch_mean] = 0.0 # Zero out values that were perviously zero
pitch /= self.pitch_std
pitch_length = torch.tensor(len(pitch)).long()
# Load energy if needed
energy, energy_length = None, None
if Energy in self.sup_data_types_set:
energy_path = self.energy_folder / f"{rel_audio_path_as_text_id}.pt"
if energy_path.exists():
energy = torch.load(energy_path).float()
else:
spec = self.get_spec(audio)
energy = torch.linalg.norm(spec.squeeze(0), axis=0).float()
torch.save(energy, energy_path)
energy_length = torch.tensor(len(energy)).long()
# Load speaker id if needed
speaker_id = None
if SpeakerID in self.sup_data_types_set:
speaker_id = torch.tensor(sample["speaker_id"]).long()
return (
audio,
audio_length,
text,
text_length,
log_mel,
log_mel_length,
durations,
align_prior_matrix,
pitch,
pitch_length,
energy,
energy_length,
speaker_id,
)
def __len__(self):
return len(self.data)
def join_data(self, data_dict):
result = []
for data_type in MAIN_DATA_TYPES + self.sup_data_types:
result.append(data_dict[data_type.name])
if issubclass(data_type, TTSDataType) and issubclass(
data_type, WithLens):
result.append(data_dict[f"{data_type.name}_lens"])
return tuple(result)
def general_collate_fn(self, batch):
(
_,
audio_lengths,
_,
tokens_lengths,
_,
log_mel_lengths,
durations_list,
align_prior_matrices_list,
pitches,
pitches_lengths,
energies,
energies_lengths,
_,
) = zip(*batch)
max_audio_len = max(audio_lengths).item()
max_tokens_len = max(tokens_lengths).item()
max_log_mel_len = max(
log_mel_lengths) if LogMel in self.sup_data_types_set else None
max_durations_len = max([
len(i) for i in durations_list
]) if Durations in self.sup_data_types_set else None
max_pitches_len = max(pitches_lengths).item(
) if Pitch in self.sup_data_types_set else None
max_energies_len = max(energies_lengths).item(
) if Energy in self.sup_data_types_set else None
if LogMel in self.sup_data_types_set:
log_mel_pad = torch.finfo(batch[0][2].dtype).tiny
align_prior_matrices = (torch.zeros(
len(align_prior_matrices_list),
max([prior_i.shape[0] for prior_i in align_prior_matrices_list]),
max([prior_i.shape[1] for prior_i in align_prior_matrices_list]),
) if AlignPriorMatrix in self.sup_data_types_set else [])
audios, tokens, log_mels, durations_list, pitches, energies, speaker_ids = [], [], [], [], [], [], []
for i, sample_tuple in enumerate(batch):
(
audio,
audio_len,
token,
token_len,
log_mel,
log_mel_len,
durations,
align_prior_matrix,
pitch,
pitch_length,
energy,
energy_length,
speaker_id,
) = sample_tuple
audio = general_padding(audio, audio_len.item(), max_audio_len)
audios.append(audio)
token = general_padding(token,
token_len.item(),
max_tokens_len,
pad_value=self.text_tokenizer_pad_id)
tokens.append(token)
if LogMel in self.sup_data_types_set:
log_mels.append(
general_padding(log_mel,
log_mel_len,
max_log_mel_len,
pad_value=log_mel_pad))
if Durations in self.sup_data_types_set:
durations_list.append(
general_padding(durations, len(durations),
max_durations_len))
if AlignPriorMatrix in self.sup_data_types_set:
align_prior_matrices[
i, :align_prior_matrix.shape[0], :align_prior_matrix.
shape[1]] = align_prior_matrix
if Pitch in self.sup_data_types_set:
pitches.append(
general_padding(pitch, pitch_length.item(),
max_pitches_len))
if Energy in self.sup_data_types_set:
energies.append(
general_padding(energy, energy_length.item(),
max_energies_len))
if SpeakerID in self.sup_data_types_set:
speaker_ids.append(speaker_id)
data_dict = {
"audio":
torch.stack(audios),
"audio_lens":
torch.stack(audio_lengths),
"text":
torch.stack(tokens),
"text_lens":
torch.stack(tokens_lengths),
"log_mel":
torch.stack(log_mels)
if LogMel in self.sup_data_types_set else None,
"log_mel_lens":
torch.stack(log_mel_lengths)
if LogMel in self.sup_data_types_set else None,
"durations":
torch.stack(durations_list)
if Durations in self.sup_data_types_set else None,
"align_prior_matrix":
align_prior_matrices
if AlignPriorMatrix in self.sup_data_types_set else None,
"pitch":
torch.stack(pitches) if Pitch in self.sup_data_types_set else None,
"pitch_lens":
torch.stack(pitches_lengths)
if Pitch in self.sup_data_types_set else None,
"energy":
torch.stack(energies)
if Energy in self.sup_data_types_set else None,
"energy_lens":
torch.stack(energies_lengths)
if Energy in self.sup_data_types_set else None,
"speaker_id":
torch.stack(speaker_ids)
if SpeakerID in self.sup_data_types_set else None,
}
return data_dict
def _collate_fn(self, batch):
data_dict = self.general_collate_fn(batch)
joined_data = self.join_data(data_dict)
return joined_data
def __init__(
self,
manifest_filepath: Union[str, Path, List[str], List[Path]],
sample_rate: int,
text_tokenizer: Union[BaseTokenizer, Callable[[str], List[int]]],
tokens: Optional[List[str]] = None,
text_normalizer: Optional[Union[Normalizer, Callable[[str],
str]]] = None,
text_normalizer_call_kwargs: Optional[Dict] = None,
text_tokenizer_pad_id: Optional[int] = None,
sup_data_types: Optional[List[str]] = None,
sup_data_path: Optional[Union[Path, str]] = None,
max_duration: Optional[float] = None,
min_duration: Optional[float] = None,
ignore_file: Optional[Union[str, Path]] = None,
trim: bool = False,
n_fft: int = 1024,
win_length: Optional[int] = None,
hop_length: Optional[int] = None,
window: str = "hann",
n_mels: int = 80,
lowfreq: int = 0,
highfreq: Optional[int] = None,
**kwargs,
):
"""Dataset which can be used for training spectrogram generators and end-to-end TTS models.
It loads main data types (audio, text) and specified supplementary data types (log mel, durations, align prior matrix, pitch, energy, speaker id).
Some of supplementary data types will be computed on the fly and saved in the sup_data_path if they did not exist before.
Saved folder can be changed for some supplementary data types (see keyword args section).
Arguments for supplementary data should be also specified in this class and they will be used from kwargs (see keyword args section).
Args:
manifest_filepath (Union[str, Path, List[str], List[Path]]): Path(s) to the .json manifests containing information on the
dataset. Each line in the .json file should be valid json. Note: the .json file itself is not valid
json. Each line should contain the following:
"audio_filepath": <PATH_TO_WAV>,
"text": <THE_TRANSCRIPT>,
"normalized_text": <NORMALIZED_TRANSCRIPT> (Optional),
"mel_filepath": <PATH_TO_LOG_MEL_PT> (Optional),
"duration": <Duration of audio clip in seconds> (Optional)
sample_rate (int): The sample rate of the audio. Or the sample rate that we will resample all files to.
text_tokenizer (Optional[Union[BaseTokenizer, Callable[[str], List[int]]]]): BaseTokenizer or callable which represents text tokenizer.
tokens (Optional[List[str]]): Tokens from text_tokenizer. Should be specified if text_tokenizer is not BaseTokenizer.
text_normalizer (Optional[Union[Normalizer, Callable[[str], str]]]): Normalizer or callable which represents text normalizer.
text_normalizer_call_kwargs (Optional[Dict]): Additional arguments for text_normalizer function.
text_tokenizer_pad_id (Optional[int]): Index of padding. Should be specified if text_tokenizer is not BaseTokenizer.
sup_data_types (Optional[List[str]]): List of supplementary data types.
sup_data_path (Optional[Union[Path, str]]): A folder that contains or will contain supplementary data (e.g. pitch).
max_duration (Optional[float]): Max duration of audio clips in seconds. All samples exceeding this will be
pruned prior to training. Note: Requires "duration" to be set in the manifest file. It does not load
audio to compute duration. Defaults to None which does not prune.
min_duration (Optional[float]): Min duration of audio clips in seconds. All samples lower than this will be
pruned prior to training. Note: Requires "duration" to be set in the manifest file. It does not load
audio to compute duration. Defaults to None which does not prune.
ignore_file (Optional[Union[str, Path]]): The location of a pickle-saved list of audio paths
that will be pruned prior to training. Defaults to None which does not prune.
trim (Optional[bool]): Whether to apply librosa.effects.trim to the audio file. Defaults to False.
n_fft (int): The number of fft samples. Defaults to 1024
win_length (Optional[int]): The length of the stft windows. Defaults to None which uses n_fft.
hop_length (Optional[int]): The hope length between fft computations. Defaults to None which uses n_fft//4.
window (str): One of 'hann', 'hamming', 'blackman','bartlett', 'none'. Which corresponds to the
equivalent torch window function.
n_mels (int): The number of mel filters. Defaults to 80.
lowfreq (int): The lowfreq input to the mel filter calculation. Defaults to 0.
highfreq (Optional[int]): The highfreq input to the mel filter calculation. Defaults to None.
Keyword Args:
log_mel_folder (Optional[Union[Path, str]]): The folder that contains or will contain log mel spectrograms.
align_prior_matrix_folder (Optional[Union[Path, str]]): The folder that contains or will contain align prior matrices.
pitch_folder (Optional[Union[Path, str]]): The folder that contains or will contain pitch.
energy_folder (Optional[Union[Path, str]]): The folder that contains or will contain energy.
durs_file (Optional[str]): String path to pickled durations location.
durs_type (Optional[str]): Type of durations. Currently supported only "aligner-based".
use_beta_binomial_interpolator (Optional[bool]): Whether to use beta-binomial interpolator for calculating alignment prior matrix. Defaults to False.
pitch_fmin (Optional[float]): The fmin input to librosa.pyin. Defaults to librosa.note_to_hz('C2').
pitch_fmax (Optional[float]): The fmax input to librosa.pyin. Defaults to librosa.note_to_hz('C7').
pitch_mean (Optional[float]): The mean that we use to normalize the pitch.
pitch_std (Optional[float]): The std that we use to normalize the pitch.
pitch_norm (Optional[bool]): Whether to normalize pitch (via pitch_mean and pitch_std) or not.
"""
super().__init__()
# Initialize text tokenizer
self.text_tokenizer = text_tokenizer
if isinstance(self.text_tokenizer, BaseTokenizer):
self.text_tokenizer_pad_id = text_tokenizer.pad
self.tokens = text_tokenizer.tokens
else:
if text_tokenizer_pad_id is None:
raise ValueError(
f"text_tokenizer_pad_id must be specified if text_tokenizer is not BaseTokenizer"
)
if tokens is None:
raise ValueError(
f"tokens must be specified if text_tokenizer is not BaseTokenizer"
)
self.text_tokenizer_pad_id = text_tokenizer_pad_id
self.tokens = tokens
# Initialize text normalizer is specified
self.text_normalizer = text_normalizer
self.text_normalizer_call = (
self.text_normalizer.normalize if isinstance(
self.text_normalizer, Normalizer) else self.text_normalizer)
self.text_normalizer_call_kwargs = (text_normalizer_call_kwargs
if text_normalizer_call_kwargs
is not None else {})
# Initialize and read manifest file(s), filter out data by duration and ignore_file, compute base dir
if isinstance(manifest_filepath, str):
manifest_filepath = [manifest_filepath]
self.manifest_filepath = manifest_filepath
data = []
total_duration = 0
for manifest_file in self.manifest_filepath:
with open(Path(manifest_file).expanduser(), 'r') as f:
logging.info(f"Loading dataset from {manifest_file}.")
for line in tqdm(f):
item = json.loads(line)
file_info = {
"audio_filepath":
item["audio_filepath"],
"original_text":
item["text"],
"mel_filepath":
item["mel_filepath"]
if "mel_filepath" in item else None,
"duration":
item["duration"] if "duration" in item else None,
"speaker_id":
item["speaker"] if "speaker" in item else None,
}
if "normalized_text" not in item:
text = item["text"]
if self.text_normalizer is not None:
text = self.text_normalizer_call(
text, **self.text_normalizer_call_kwargs)
file_info["normalized_text"] = text
file_info["text_tokens"] = self.text_tokenizer(text)
else:
file_info["normalized_text"] = item["normalized_text"]
file_info["text_tokens"] = self.text_tokenizer(
item["normalized_text"])
data.append(file_info)
if file_info["duration"] is None:
logging.info(
"Not all audio files have duration information. Duration logging will be disabled."
)
total_duration = None
if total_duration is not None:
total_duration += item["duration"]
logging.info(f"Loaded dataset with {len(data)} files.")
if total_duration is not None:
logging.info(
f"Dataset contains {total_duration / 3600:.2f} hours.")
self.data = TTSDataset.filter_files(data, ignore_file, min_duration,
max_duration, total_duration)
self.base_data_dir = get_base_dir(
[item["audio_filepath"] for item in self.data])
# Initialize audio and mel related parameters
self.sample_rate = sample_rate
self.featurizer = WaveformFeaturizer(sample_rate=self.sample_rate)
self.trim = trim
self.n_fft = n_fft
self.n_mels = n_mels
self.lowfreq = lowfreq
self.highfreq = highfreq
self.window = window
self.win_length = win_length or self.n_fft
self.hop_length = hop_length
self.hop_len = self.hop_length or self.n_fft // 4
self.fb = torch.tensor(
librosa.filters.mel(self.sample_rate,
self.n_fft,
n_mels=self.n_mels,
fmin=self.lowfreq,
fmax=self.highfreq),
dtype=torch.float,
).unsqueeze(0)
window_fn = {
'hann': torch.hann_window,
'hamming': torch.hamming_window,
'blackman': torch.blackman_window,
'bartlett': torch.bartlett_window,
'none': None,
}.get(self.window, None)
self.stft = lambda x: torch.stft(
input=x,
n_fft=self.n_fft,
hop_length=self.hop_len,
win_length=self.win_length,
window=window_fn(self.win_length, periodic=False).to(torch.float)
if window_fn else None,
)
# Initialize sup_data_path, sup_data_types and run preprocessing methods for every supplementary data type
if sup_data_path is not None:
Path(sup_data_path).mkdir(parents=True, exist_ok=True)
self.sup_data_path = sup_data_path
self.sup_data_types = ([
DATA_STR2DATA_CLASS[d_as_str] for d_as_str in sup_data_types
] if sup_data_types is not None else [])
self.sup_data_types_set = set(self.sup_data_types)
for data_type in self.sup_data_types:
if data_type not in VALID_SUPPLEMENTARY_DATA_TYPES:
raise NotImplementedError(
f"Current implementation doesn't support {data_type} type."
)
getattr(self, f"add_{data_type.name}")(**kwargs)
def __init__(
self,
manifest_filepath: str,
mappings_filepath: str,
sample_rate: int,
max_duration: Optional[float] = None,
min_duration: Optional[float] = None,
ignore_file: Optional[str] = None,
trim: bool = False,
load_supplementary_values=True, # Set to False for validation
):
"""
Dataset that loads audio, phonemes and their durations, pitches per frame, and energies per frame
for FastSpeech 2 from paths described in a JSON manifest (see the AudioDataset documentation for details
on the manifest format), as well as a mappings file for word to phones and phones to indices.
The text in the manifest is ignored; instead, the phoneme indices for prediction come from the
duration files.
For each sample, paths for duration, energy, and pitch files are inferred from the manifest's audio
filepaths by replacing '/wavs' with '/phoneme_durations', '/pitches', and '/energies', and swapping out
the file extension to '.pt', '.npy', and '.npy' respectively.
For example, given manifest audio path `/data/LJSpeech/wavs/LJ001-0001.wav`, the inferred duration and
phonemes file path would be `/data/LJSpeech/phoneme_durations/LJ001-0001.pt`.
Note that validation datasets only need the audio files and phoneme & duration files, set
`load_supplementary_values` to False for validation sets.
Args:
manifest_filepath (str): Path to the JSON manifest file that lists audio files.
mappings_filepath (str): Path to a JSON mappings file that contains mappings "word2phones" and
"phone2idx". The latter is used to determine the padding index.
sample_rate (int): Target sample rate of the audio.
max_duration (float): If audio exceeds this length in seconds, it is filtered from the dataset.
Defaults to None, which does not filter any audio.
min_duration (float): If audio is shorter than this length in seconds, it is filtered from the dataset.
Defaults to None, which does not filter any audio.
ignore_file (str): Optional pickled file which contains a list of files to ignore (e.g. files that
contain OOV words).
Defaults to None.
trim (bool): Whether to use librosa.effects.trim on the audio clip.
Defaults to False.
load_supplementary_values (bool): Whether or not to load pitch and energy files. Set this to False for
validation datasets.
Defaults to True.
"""
super().__init__()
# Retrieve mappings from file
with open(mappings_filepath, 'r') as f:
mappings = json.load(f)
self.word2phones = mappings['word2phones']
self.phone2idx = mappings['phone2idx']
# Load data from manifests
audio_files = []
total_duration = 0
if isinstance(manifest_filepath, str):
manifest_filepath = [manifest_filepath]
for manifest_file in manifest_filepath:
with open(expanduser(manifest_file), 'r') as f:
logging.info(f"Loading dataset from {manifest_file}.")
for line in f:
item = json.loads(line)
audio_files.append({"audio_filepath": item["audio_filepath"], "duration": item["duration"]})
total_duration += item["duration"]
total_dataset_len = len(audio_files)
logging.info(f"Loaded dataset with {total_dataset_len} files totalling {total_duration/3600:.2f} hours.")
self.data = []
if load_supplementary_values:
dataitem = py_collections.namedtuple(
typename='AudioTextEntity', field_names='audio_file duration text_tokens pitches energies'
)
else:
dataitem = py_collections.namedtuple(
typename='AudioTextEntity', field_names='audio_file duration text_tokens'
)
if ignore_file:
logging.info(f"using {ignore_file} to prune dataset.")
with open(ignore_file, "rb") as f:
wavs_to_ignore = set(pickle.load(f))
pruned_duration = 0
pruned_items = 0
for item in audio_files:
audio_path = item['audio_filepath']
LJ_id = os.path.splitext(os.path.basename(audio_path))[0]
# Prune data according to min/max_duration & the ignore file
if (min_duration and item["duration"] < min_duration) or (
max_duration and item["duration"] > max_duration
):
pruned_duration += item["duration"]
pruned_items += 1
continue
if ignore_file and (LJ_id in wavs_to_ignore):
pruned_items += 1
pruned_duration += item["duration"]
wavs_to_ignore.remove(LJ_id)
continue
# Else not pruned, load additional info
# Phoneme durations and text token indices from durations file
dur_path = audio_path.replace('/wavs/', '/phoneme_durations/').replace('.wav', '.pt')
duration_info = torch.load(dur_path)
durs = duration_info['token_duration']
text_tokens = duration_info['text_encoded']
if load_supplementary_values:
# Load pitch file (F0s)
pitch_path = audio_path.replace('/wavs/', '/pitches/').replace('.wav', '.npy')
pitches = torch.from_numpy(np.load(pitch_path).astype(dtype='float32'))
# Load energy file (L2-norm of the amplitude of each STFT frame of an utterance)
energies_path = audio_path.replace('/wavs/', '/energies/').replace('.wav', '.npy')
energies = torch.from_numpy(np.load(energies_path))
self.data.append(
dataitem(
audio_file=item['audio_filepath'],
duration=durs,
pitches=torch.clamp(pitches, min=1e-5),
energies=energies,
text_tokens=text_tokens,
)
)
else:
self.data.append(dataitem(audio_file=item['audio_filepath'], duration=durs, text_tokens=text_tokens,))
logging.info(f"Pruned {pruned_items} files and {pruned_duration/3600:.2f} hours.")
logging.info(
f"Final dataset contains {len(self.data)} files and {(total_duration-pruned_duration)/3600:.2f} hours."
)
self.featurizer = WaveformFeaturizer(sample_rate=sample_rate)
self.trim = trim
self.load_supplementary_values = load_supplementary_values
def _setup_dataloader_from_config(self, config: DictConfig):
OmegaConf.set_struct(config, False)
config.is_regression_task = self.is_regression_task
OmegaConf.set_struct(config, True)
if 'augmentor' in config:
augmentor = process_augmentations(config['augmentor'])
else:
augmentor = None
featurizer = WaveformFeaturizer(
sample_rate=config['sample_rate'], int_values=config.get('int_values', False), augmentor=augmentor
)
shuffle = config['shuffle']
# Instantiate tarred dataset loader or normal dataset loader
if config.get('is_tarred', False):
if ('tarred_audio_filepaths' in config and config['tarred_audio_filepaths'] is None) or (
'manifest_filepath' in config and config['manifest_filepath'] is None
):
logging.warning(
"Could not load dataset as `manifest_filepath` is None or "
f"`tarred_audio_filepaths` is None. Provided config : {config}"
)
return None
if 'vad_stream' in config and config['vad_stream']:
logging.warning("VAD inference does not support tarred dataset now")
return None
shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0
dataset = audio_to_label_dataset.get_tarred_classification_label_dataset(
featurizer=featurizer,
config=OmegaConf.to_container(config),
shuffle_n=shuffle_n,
global_rank=self.global_rank,
world_size=self.world_size,
)
shuffle = False
batch_size = config['batch_size']
collate_func = dataset.collate_fn
else:
if 'manifest_filepath' in config and config['manifest_filepath'] is None:
logging.warning(f"Could not load dataset as `manifest_filepath` is None. Provided config : {config}")
return None
if 'vad_stream' in config and config['vad_stream']:
logging.info("Perform streaming frame-level VAD")
dataset = audio_to_label_dataset.get_speech_label_dataset(
featurizer=featurizer, config=OmegaConf.to_container(config)
)
batch_size = 1
collate_func = dataset.vad_frame_seq_collate_fn
else:
dataset = audio_to_label_dataset.get_classification_label_dataset(
featurizer=featurizer, config=OmegaConf.to_container(config)
)
batch_size = config['batch_size']
collate_func = dataset.collate_fn
return torch.utils.data.DataLoader(
dataset=dataset,
batch_size=batch_size,
collate_fn=collate_func,
drop_last=config.get('drop_last', False),
shuffle=shuffle,
num_workers=config.get('num_workers', 0),
pin_memory=config.get('pin_memory', False),
)
def __init__(
self,
manifest_filepath: str,
sample_rate: int,
text_tokenizer: Union[BaseTokenizer, Callable[[str], List[int]]],
tokens: Optional[List[str]] = None,
text_normalizer: Optional[Union[Normalizer, Callable[[str],
str]]] = None,
text_normalizer_call_args: Optional[Dict] = None,
text_tokenizer_pad_id: Optional[int] = None,
sup_data_types: Optional[List[str]] = None,
sup_data_path: Optional[Union[Path, str]] = None,
max_duration: Optional[float] = None,
min_duration: Optional[float] = None,
ignore_file: Optional[str] = None,
trim: bool = False,
n_fft=1024,
win_length=None,
hop_length=None,
window="hann",
n_mels=80,
lowfreq=0,
highfreq=None,
**kwargs,
):
"""Dataset that loads main data types (audio and text) and specified supplementary data types (e.g. log mel, durations, pitch).
Most supplementary data types will be computed on the fly and saved in the supplementary_folder if they did not exist before.
Arguments for supplementary data should be also specified in this class and they will be used from kwargs (see keyword args section).
Args:
manifest_filepath (str, Path, List[str, Path]): Path(s) to the .json manifests containing information on the
dataset. Each line in the .json file should be valid json. Note: the .json file itself is not valid
json. Each line should contain the following:
"audio_filepath": <PATH_TO_WAV>
"mel_filepath": <PATH_TO_LOG_MEL_PT> (Optional)
"duration": <Duration of audio clip in seconds> (Optional)
"text": <THE_TRANSCRIPT> (Optional)
sample_rate (int): The sample rate of the audio. Or the sample rate that we will resample all files to.
text_tokenizer (Optional[Union[BaseTokenizer, Callable[[str], List[int]]]]): BaseTokenizer or callable which represents text tokenizer.
tokens (Optional[List[str]]): Tokens from text_tokenizer. Should be specified if text_tokenizer is not BaseTokenizer.
text_normalizer (Optional[Union[Normalizer, Callable[[str], str]]]): Normalizer or callable which represents text normalizer.
text_normalizer_call_args (Optional[Dict]): Additional arguments for text_normalizer function.
text_tokenizer_pad_id (Optional[int]): Index of padding. Should be specified if text_tokenizer is not BaseTokenizer.
sup_data_types (Optional[List[str]]): List of supplementary data types.
sup_data_path (Optional[Union[Path, str]]): A folder that contains or will contain supplementary data (e.g. pitch).
max_duration (Optional[float]): Max duration of audio clips in seconds. All samples exceeding this will be
pruned prior to training. Note: Requires "duration" to be set in the manifest file. It does not load
audio to compute duration. Defaults to None which does not prune.
min_duration (Optional[float]): Min duration of audio clips in seconds. All samples lower than this will be
pruned prior to training. Note: Requires "duration" to be set in the manifest file. It does not load
audio to compute duration. Defaults to None which does not prune.
ignore_file (Optional[str, Path]): The location of a pickle-saved list of audio_ids (the stem of the audio
files) that will be pruned prior to training. Defaults to None which does not prune.
trim (Optional[bool]): Whether to apply librosa.effects.trim to the audio file. Defaults to False.
n_fft (Optional[int]): The number of fft samples. Defaults to 1024
win_length (Optional[int]): The length of the stft windows. Defaults to None which uses n_fft.
hop_length (Optional[int]): The hope length between fft computations. Defaults to None which uses n_fft//4.
window (Optional[str]): One of 'hann', 'hamming', 'blackman','bartlett', 'none'. Which corresponds to the
equivalent torch window function.
n_mels (Optional[int]): The number of mel filters. Defaults to 80.
lowfreq (Optional[int]): The lowfreq input to the mel filter calculation. Defaults to 0.
highfreq (Optional[int]): The highfreq input to the mel filter calculation. Defaults to None.
Keyword Args:
durs_file (Optional[str]): String path to pickled durations location.
durs_type (Optional[str]): Type of durations. Currently supported only "aligned-based".
use_beta_binomial_interpolator (Optional[bool]): Whether to use beta-binomial interpolator. Defaults to False.
pitch_fmin (Optional[float]): The fmin input to librosa.pyin. Defaults to librosa.note_to_hz('C2').
pitch_fmax (Optional[float]): The fmax input to librosa.pyin. Defaults to librosa.note_to_hz('C7').
pitch_avg (Optional[float]): The mean that we use to normalize the pitch.
pitch_std (Optional[float]): The std that we use to normalize the pitch.
pitch_norm (Optional[bool]): Whether to normalize pitch (via pitch_avg and pitch_std) or not.
"""
super().__init__()
self.text_normalizer = text_normalizer
self.text_normalizer_call = (
self.text_normalizer.normalize if isinstance(
self.text_normalizer, Normalizer) else self.text_normalizer)
self.text_normalizer_call_args = text_normalizer_call_args if text_normalizer_call_args is not None else {}
self.text_tokenizer = text_tokenizer
if isinstance(self.text_tokenizer, BaseTokenizer):
self.text_tokenizer_pad_id = text_tokenizer.pad
self.tokens = text_tokenizer.tokens
else:
if text_tokenizer_pad_id is None:
raise ValueError(
f"text_tokenizer_pad_id must be specified if text_tokenizer is not BaseTokenizer"
)
if tokens is None:
raise ValueError(
f"tokens must be specified if text_tokenizer is not BaseTokenizer"
)
self.text_tokenizer_pad_id = text_tokenizer_pad_id
self.tokens = tokens
if isinstance(manifest_filepath, str):
manifest_filepath = [manifest_filepath]
self.manifest_filepath = manifest_filepath
if sup_data_path is not None:
Path(sup_data_path).mkdir(parents=True, exist_ok=True)
self.sup_data_path = sup_data_path
self.sup_data_types = ([
DATA_STR2DATA_CLASS[d_as_str] for d_as_str in sup_data_types
] if sup_data_types is not None else [])
self.sup_data_types_set = set(self.sup_data_types)
self.data = []
audio_files = []
total_duration = 0
for manifest_file in self.manifest_filepath:
with open(Path(manifest_file).expanduser(), 'r') as f:
logging.info(f"Loading dataset from {manifest_file}.")
for line in tqdm(f):
item = json.loads(line)
file_info = {
"audio_filepath":
item["audio_filepath"],
"mel_filepath":
item["mel_filepath"]
if "mel_filepath" in item else None,
"duration":
item["duration"] if "duration" in item else None,
"text_tokens":
None,
"speaker_id":
item["speaker"] if "speaker" in item else None,
}
if "text" in item:
text = item["text"]
if self.text_normalizer is not None:
text = self.text_normalizer_call(
text, **self.text_normalizer_call_args)
text_tokens = self.text_tokenizer(text)
file_info["raw_text"] = item["text"]
file_info["text_tokens"] = text_tokens
audio_files.append(file_info)
if file_info["duration"] is None:
logging.info(
"Not all audio files have duration information. Duration logging will be disabled."
)
total_duration = None
if total_duration is not None:
total_duration += item["duration"]
logging.info(f"Loaded dataset with {len(audio_files)} files.")
if total_duration is not None:
logging.info(
f"Dataset contains {total_duration / 3600:.2f} hours.")
if ignore_file:
logging.info(f"using {ignore_file} to prune dataset.")
with open(Path(ignore_file).expanduser(), "rb") as f:
wavs_to_ignore = set(pickle.load(f))
pruned_duration = 0 if total_duration is not None else None
pruned_items = 0
for item in audio_files:
audio_path = item['audio_filepath']
audio_id = Path(audio_path).stem
# Prune data according to min/max_duration & the ignore file
if total_duration is not None:
if (min_duration and item["duration"] < min_duration) or (
max_duration and item["duration"] > max_duration):
pruned_duration += item["duration"]
pruned_items += 1
continue
if ignore_file and (audio_id in wavs_to_ignore):
pruned_items += 1
pruned_duration += item["duration"]
wavs_to_ignore.remove(audio_id)
continue
self.data.append(item)
logging.info(
f"Pruned {pruned_items} files. Final dataset contains {len(self.data)} files"
)
if pruned_duration is not None:
logging.info(
f"Pruned {pruned_duration / 3600:.2f} hours. Final dataset contains "
f"{(total_duration - pruned_duration) / 3600:.2f} hours.")
self.sample_rate = sample_rate
self.featurizer = WaveformFeaturizer(sample_rate=self.sample_rate)
self.trim = trim
self.n_fft = n_fft
self.n_mels = n_mels
self.lowfreq = lowfreq
self.highfreq = highfreq
self.window = window
self.win_length = win_length or self.n_fft
self.hop_length = hop_length
self.hop_len = self.hop_length or self.n_fft // 4
self.fb = torch.tensor(
librosa.filters.mel(self.sample_rate,
self.n_fft,
n_mels=self.n_mels,
fmin=self.lowfreq,
fmax=self.highfreq),
dtype=torch.float,
).unsqueeze(0)
window_fn = {
'hann': torch.hann_window,
'hamming': torch.hamming_window,
'blackman': torch.blackman_window,
'bartlett': torch.bartlett_window,
'none': None,
}.get(self.window, None)
self.stft = lambda x: torch.stft(
input=x,
n_fft=self.n_fft,
hop_length=self.hop_len,
win_length=self.win_length,
window=window_fn(self.win_length, periodic=False).to(torch.float)
if window_fn else None,
)
for data_type in self.sup_data_types:
if data_type not in VALID_SUPPLEMENTARY_DATA_TYPES:
raise NotImplementedError(
f"Current implementation of TTSDataset doesn't support {data_type} type."
)
getattr(self, f"add_{data_type.name}")(**kwargs)
class FastSpeech2Dataset(Dataset):
@property
def output_types(self) -> Optional[Dict[str, NeuralType]]:
"""Returns definitions of module output ports."""
return {
'audio_signal': NeuralType(('B', 'T'), AudioSignal()),
'a_sig_length': NeuralType(('B'), LengthsType()),
'transcripts': NeuralType(('B', 'T'), TokenIndex()),
'transcript_length': NeuralType(('B'), LengthsType()),
'durations': NeuralType(('B', 'T'), TokenDurationType()),
'pitches': NeuralType(('B', 'T'), RegressionValuesType()),
'energies': NeuralType(('B', 'T'), RegressionValuesType()),
}
def __init__(
self,
manifest_filepath: str,
mappings_filepath: str,
sample_rate: int,
max_duration: Optional[float] = None,
min_duration: Optional[float] = None,
ignore_file: Optional[str] = None,
trim: bool = False,
load_supplementary_values=True, # Set to False for validation
):
"""
Dataset that loads audio, phonemes and their durations, pitches per frame, and energies per frame
for FastSpeech 2 from paths described in a JSON manifest (see the AudioDataset documentation for details
on the manifest format), as well as a mappings file for word to phones and phones to indices.
The text in the manifest is ignored; instead, the phoneme indices for prediction come from the
duration files.
For each sample, paths for duration, energy, and pitch files are inferred from the manifest's audio
filepaths by replacing '/wavs' with '/phoneme_durations', '/pitches', and '/energies', and swapping out
the file extension to '.pt', '.npy', and '.npy' respectively.
For example, given manifest audio path `/data/LJSpeech/wavs/LJ001-0001.wav`, the inferred duration and
phonemes file path would be `/data/LJSpeech/phoneme_durations/LJ001-0001.pt`.
Note that validation datasets only need the audio files and phoneme & duration files, set
`load_supplementary_values` to False for validation sets.
Args:
manifest_filepath (str): Path to the JSON manifest file that lists audio files.
mappings_filepath (str): Path to a JSON mappings file that contains mappings "word2phones" and
"phone2idx". The latter is used to determine the padding index.
sample_rate (int): Target sample rate of the audio.
max_duration (float): If audio exceeds this length in seconds, it is filtered from the dataset.
Defaults to None, which does not filter any audio.
min_duration (float): If audio is shorter than this length in seconds, it is filtered from the dataset.
Defaults to None, which does not filter any audio.
ignore_file (str): Optional pickled file which contains a list of files to ignore (e.g. files that
contain OOV words).
Defaults to None.
trim (bool): Whether to use librosa.effects.trim on the audio clip.
Defaults to False.
load_supplementary_values (bool): Whether or not to load pitch and energy files. Set this to False for
validation datasets.
Defaults to True.
"""
super().__init__()
# Retrieve mappings from file
with open(mappings_filepath, 'r') as f:
mappings = json.load(f)
self.word2phones = mappings['word2phones']
self.phone2idx = mappings['phone2idx']
# Load data from manifests
audio_files = []
total_duration = 0
if isinstance(manifest_filepath, str):
manifest_filepath = [manifest_filepath]
for manifest_file in manifest_filepath:
with open(expanduser(manifest_file), 'r') as f:
logging.info(f"Loading dataset from {manifest_file}.")
for line in f:
item = json.loads(line)
audio_files.append({"audio_filepath": item["audio_filepath"], "duration": item["duration"]})
total_duration += item["duration"]
total_dataset_len = len(audio_files)
logging.info(f"Loaded dataset with {total_dataset_len} files totalling {total_duration/3600:.2f} hours.")
self.data = []
if load_supplementary_values:
dataitem = py_collections.namedtuple(
typename='AudioTextEntity', field_names='audio_file duration text_tokens pitches energies'
)
else:
dataitem = py_collections.namedtuple(
typename='AudioTextEntity', field_names='audio_file duration text_tokens'
)
if ignore_file:
logging.info(f"using {ignore_file} to prune dataset.")
with open(ignore_file, "rb") as f:
wavs_to_ignore = set(pickle.load(f))
pruned_duration = 0
pruned_items = 0
for item in audio_files:
audio_path = item['audio_filepath']
LJ_id = os.path.splitext(os.path.basename(audio_path))[0]
# Prune data according to min/max_duration & the ignore file
if (min_duration and item["duration"] < min_duration) or (
max_duration and item["duration"] > max_duration
):
pruned_duration += item["duration"]
pruned_items += 1
continue
if ignore_file and (LJ_id in wavs_to_ignore):
pruned_items += 1
pruned_duration += item["duration"]
wavs_to_ignore.remove(LJ_id)
continue
# Else not pruned, load additional info
# Phoneme durations and text token indices from durations file
dur_path = audio_path.replace('/wavs/', '/phoneme_durations/').replace('.wav', '.pt')
duration_info = torch.load(dur_path)
durs = duration_info['token_duration']
text_tokens = duration_info['text_encoded']
if load_supplementary_values:
# Load pitch file (F0s)
pitch_path = audio_path.replace('/wavs/', '/pitches/').replace('.wav', '.npy')
pitches = torch.from_numpy(np.load(pitch_path).astype(dtype='float32'))
# Load energy file (L2-norm of the amplitude of each STFT frame of an utterance)
energies_path = audio_path.replace('/wavs/', '/energies/').replace('.wav', '.npy')
energies = torch.from_numpy(np.load(energies_path))
self.data.append(
dataitem(
audio_file=item['audio_filepath'],
duration=durs,
pitches=torch.clamp(pitches, min=1e-5),
energies=energies,
text_tokens=text_tokens,
)
)
else:
self.data.append(dataitem(audio_file=item['audio_filepath'], duration=durs, text_tokens=text_tokens,))
logging.info(f"Pruned {pruned_items} files and {pruned_duration/3600:.2f} hours.")
logging.info(
f"Final dataset contains {len(self.data)} files and {(total_duration-pruned_duration)/3600:.2f} hours."
)
self.featurizer = WaveformFeaturizer(sample_rate=sample_rate)
self.trim = trim
self.load_supplementary_values = load_supplementary_values
def __getitem__(self, index):
sample = self.data[index]
features = self.featurizer.process(sample.audio_file, trim=self.trim)
f, fl = features, torch.tensor(features.shape[0]).long()
t, tl = sample.text_tokens.long(), torch.tensor(len(sample.text_tokens)).long()
if self.load_supplementary_values:
return f, fl, t, tl, sample.duration, sample.pitches, sample.energies
else:
return f, fl, t, tl, sample.duration, None, None
def __len__(self):
return len(self.data)
def _collate_fn(self, batch):
pad_id = len(self.phone2idx)
if self.load_supplementary_values:
_, audio_lengths, _, tokens_lengths, duration, pitches, energies = zip(*batch)
else:
_, audio_lengths, _, tokens_lengths, duration, _, _ = zip(*batch)
max_audio_len = 0
max_audio_len = max(audio_lengths).item()
max_tokens_len = max(tokens_lengths).item()
max_durations_len = max([len(i) for i in duration])
max_duration_sum = max([sum(i) for i in duration])
if self.load_supplementary_values:
max_pitches_len = max([len(i) for i in pitches])
max_energies_len = max([len(i) for i in energies])
if max_pitches_len != max_energies_len or max_pitches_len != max_duration_sum:
logging.warning(
f"max_pitches_len: {max_pitches_len} != max_energies_len: {max_energies_len} != "
f"max_duration_sum:{max_duration_sum}. Your training run will error out!"
)
# Add padding where necessary
audio_signal, tokens, duration_batched, pitches_batched, energies_batched = [], [], [], [], []
for sample_tuple in batch:
if self.load_supplementary_values:
sig, sig_len, tokens_i, tokens_i_len, duration, pitch, energy = sample_tuple
else:
sig, sig_len, tokens_i, tokens_i_len, duration, _, _ = sample_tuple
sig_len = sig_len.item()
if sig_len < max_audio_len:
pad = (0, max_audio_len - sig_len)
sig = torch.nn.functional.pad(sig, pad)
audio_signal.append(sig)
tokens_i_len = tokens_i_len.item()
if tokens_i_len < max_tokens_len:
pad = (0, max_tokens_len - tokens_i_len)
tokens_i = torch.nn.functional.pad(tokens_i, pad, value=pad_id)
tokens.append(tokens_i)
if len(duration) < max_durations_len:
pad = (0, max_durations_len - len(duration))
duration = torch.nn.functional.pad(duration, pad)
duration_batched.append(duration)
if self.load_supplementary_values:
pitch = pitch.squeeze(0)
if len(pitch) < max_pitches_len:
pad = (0, max_pitches_len - len(pitch))
pitch = torch.nn.functional.pad(pitch.squeeze(0), pad)
pitches_batched.append(pitch)
if len(energy) < max_energies_len:
pad = (0, max_energies_len - len(energy))
energy = torch.nn.functional.pad(energy, pad)
energies_batched.append(energy)
audio_signal = torch.stack(audio_signal)
audio_lengths = torch.stack(audio_lengths)
tokens = torch.stack(tokens)
tokens_lengths = torch.stack(tokens_lengths)
duration_batched = torch.stack(duration_batched)
if self.load_supplementary_values:
pitches_batched = torch.stack(pitches_batched)
energies_batched = torch.stack(energies_batched)
assert pitches_batched.shape == energies_batched.shape
return (
audio_signal,
audio_lengths,
tokens,
tokens_lengths,
duration_batched,
pitches_batched,
energies_batched,
)
return (audio_signal, audio_lengths, tokens, tokens_lengths, duration_batched, None, None)
class _AudioTextDataset(Dataset):
"""
Dataset that loads tensors via a json file containing paths to audio files, transcripts, and durations (in seconds).
Each new line is a different sample. Example below:
{"audio_filepath": "/path/to/audio.wav", "text_filepath": "/path/to/audio.txt", "duration": 23.147}
...
{"audio_filepath": "/path/to/audio.wav", "text": "the transcription", "offset": 301.75, "duration": 0.82, "utt":
"utterance_id", "ctm_utt": "en_4156", "side": "A"}
Args:
manifest_filepath: Path to manifest json as described above. Can be comma-separated paths.
parser: Str for a language specific preprocessor or a callable.
sample_rate (int): Sample rate to resample loaded audio to
int_values (bool): If true, load samples as 32-bit integers. Defauts to False.
augmentor (nemo.collections.asr.parts.perturb.AudioAugmentor): An AudioAugmentor object used to augment loaded
audio
max_duration: If audio exceeds this length, do not include in dataset
min_duration: If audio is less than this length, do not include in dataset
max_utts: Limit number of utterances
trim: whether or not to trim silence. Defaults to False
bos_id: Id of beginning of sequence symbol to append if not None
eos_id: Id of end of sequence symbol to append if not None
pad_id: Id of pad symbol. Defaults to 0
return_sample_id (bool): whether to return the sample_id as a part of each sample
"""
@property
def output_types(self) -> Optional[Dict[str, NeuralType]]:
"""Returns definitions of module output ports.
"""
return {
'audio_signal': NeuralType(('B', 'T'), AudioSignal()),
'a_sig_length': NeuralType(tuple('B'), LengthsType()),
'transcripts': NeuralType(('B', 'T'), LabelsType()),
'transcript_length': NeuralType(tuple('B'), LengthsType()),
'sample_id': NeuralType(tuple('B'), LengthsType(), optional=True),
}
def __init__(
self,
manifest_filepath: str,
parser: Union[str, Callable],
sample_rate: int,
int_values: bool = False,
augmentor: 'nemo.collections.asr.parts.perturb.AudioAugmentor' = None,
max_duration: Optional[int] = None,
min_duration: Optional[int] = None,
max_utts: int = 0,
trim: bool = False,
bos_id: Optional[int] = None,
eos_id: Optional[int] = None,
pad_id: int = 0,
return_sample_id: bool = False,
):
if type(manifest_filepath) == str:
manifest_filepath = manifest_filepath.split(",")
self.manifest_processor = ASRManifestProcessor(
manifest_filepath=manifest_filepath,
parser=parser,
max_duration=max_duration,
min_duration=min_duration,
max_utts=max_utts,
bos_id=bos_id,
eos_id=eos_id,
pad_id=pad_id,
)
self.featurizer = WaveformFeaturizer(sample_rate=sample_rate,
int_values=int_values,
augmentor=augmentor)
self.trim = trim
self.return_sample_id = return_sample_id
def get_manifest_sample(self, sample_id):
return self.manifest_processor.collection[sample_id]
def __getitem__(self, index):
sample = self.manifest_processor.collection[index]
offset = sample.offset
if offset is None:
offset = 0
features = self.featurizer.process(sample.audio_file,
offset=offset,
duration=sample.duration,
trim=self.trim,
orig_sr=sample.orig_sr)
f, fl = features, torch.tensor(features.shape[0]).long()
t, tl = self.manifest_processor.process_text_by_sample(sample=sample)
if self.return_sample_id:
output = f, fl, torch.tensor(t).long(), torch.tensor(
tl).long(), index
else:
output = f, fl, torch.tensor(t).long(), torch.tensor(tl).long()
return output
def __len__(self):
return len(self.manifest_processor.collection)
def _collate_fn(self, batch):
return _speech_collate_fn(batch, pad_id=self.manifest_processor.pad_id)
class _TarredAudioToTextDataset(IterableDataset):
"""
A similar Dataset to the AudioToCharDataset/AudioToBPEDataset, but which loads tarred audio files.
Accepts a single comma-separated JSON manifest file (in the same style as for the AudioToCharDataset/AudioToBPEDataset),
as well as the path(s) to the tarball(s) containing the wav files. Each line of the manifest should
contain the information for one audio file, including at least the transcript and name of the audio
file within the tarball.
Valid formats for the audio_tar_filepaths argument include:
(1) a single string that can be brace-expanded, e.g. 'path/to/audio.tar' or 'path/to/audio_{1..100}.tar.gz', or
(2) a list of file paths that will not be brace-expanded, e.g. ['audio_1.tar', 'audio_2.tar', ...].
Note: For brace expansion in (1), there may be cases where `{x..y}` syntax cannot be used due to shell interference.
This occurs most commonly inside SLURM scripts. Therefore we provide a few equivalent replacements.
Supported opening braces - { <=> (, [, < and the special tag _OP_.
Supported closing braces - } <=> ), ], > and the special tag _CL_.
For SLURM based tasks, we suggest the use of the special tags for ease of use.
See the WebDataset documentation for more information about accepted data and input formats.
If using multiple workers the number of shards should be divisible by world_size to ensure an
even split among workers. If it is not divisible, logging will give a warning but training will proceed.
In addition, if using mutiprocessing, each shard MUST HAVE THE SAME NUMBER OF ENTRIES after filtering
is applied. We currently do not check for this, but your program may hang if the shards are uneven!
Notice that a few arguments are different from the AudioToCharDataset; for example, shuffle (bool) has been
replaced by shuffle_n (int).
Additionally, please note that the len() of this DataLayer is assumed to be the length of the manifest
after filtering. An incorrect manifest length may lead to some DataLoader issues down the line.
Args:
audio_tar_filepaths: Either a list of audio tarball filepaths, or a
string (can be brace-expandable).
manifest_filepath (str): Path to the manifest.
parser (callable): A callable which is used to pre-process the text output.
sample_rate (int): Sample rate to resample loaded audio to
int_values (bool): If true, load samples as 32-bit integers. Defauts to False.
augmentor (nemo.collections.asr.parts.perturb.AudioAugmentor): An AudioAugmentor
object used to augment loaded audio
shuffle_n (int): How many samples to look ahead and load to be shuffled.
See WebDataset documentation for more details.
Defaults to 0.
min_duration (float): Dataset parameter.
All training files which have a duration less than min_duration
are dropped. Note: Duration is read from the manifest JSON.
Defaults to 0.1.
max_duration (float): Dataset parameter.
All training files which have a duration more than max_duration
are dropped. Note: Duration is read from the manifest JSON.
Defaults to None.
max_utts (int): Limit number of utterances. 0 means no maximum.
blank_index (int): Blank character index, defaults to -1.
unk_index (int): Unknown character index, defaults to -1.
normalize (bool): Dataset parameter.
Whether to use automatic text cleaning.
It is highly recommended to manually clean text for best results.
Defaults to True.
trim (bool): Whether to use trim silence from beginning and end
of audio signal using librosa.effects.trim().
Defaults to False.
bos_id (id): Dataset parameter.
Beginning of string symbol id used for seq2seq models.
Defaults to None.
eos_id (id): Dataset parameter.
End of string symbol id used for seq2seq models.
Defaults to None.
pad_id (id): Token used to pad when collating samples in batches.
If this is None, pads using 0s.
Defaults to None.
shard_strategy (str): Tarred dataset shard distribution strategy chosen as a str value during ddp.
- `scatter`: The default shard strategy applied by WebDataset, where each node gets
a unique set of shards, which are permanently pre-allocated and never changed at runtime.
- `replicate`: Optional shard strategy, where each node gets all of the set of shards
available in the tarred dataset, which are permanently pre-allocated and never changed at runtime.
The benefit of replication is that it allows each node to sample data points from the entire
dataset independently of other nodes, and reduces dependence on value of `shuffle_n`.
Note: Replicated strategy allows every node to sample the entire set of available tarfiles,
and therefore more than one node may sample the same tarfile, and even sample the same
data points! As such, there is no assured guarantee that all samples in the dataset will be
sampled at least once during 1 epoch.
global_rank (int): Worker rank, used for partitioning shards. Defaults to 0.
world_size (int): Total number of processes, used for partitioning shards. Defaults to 0.
return_sample_id (bool): whether to return the sample_id as a part of each sample
"""
def __init__(
self,
audio_tar_filepaths: Union[str, List[str]],
manifest_filepath: str,
parser: Callable,
sample_rate: int,
int_values: bool = False,
augmentor: Optional[
'nemo.collections.asr.parts.perturb.AudioAugmentor'] = None,
shuffle_n: int = 0,
min_duration: Optional[float] = None,
max_duration: Optional[float] = None,
max_utts: int = 0,
trim: bool = False,
bos_id: Optional[int] = None,
eos_id: Optional[int] = None,
pad_id: int = 0,
shard_strategy: str = "scatter",
global_rank: int = 0,
world_size: int = 0,
return_sample_id: bool = False,
):
self.manifest_processor = ASRManifestProcessor(
manifest_filepath=manifest_filepath,
parser=parser,
max_duration=max_duration,
min_duration=min_duration,
max_utts=max_utts,
bos_id=bos_id,
eos_id=eos_id,
pad_id=pad_id,
index_by_file_id=
True, # Must set this so the manifest lines can be indexed by file ID
)
self.featurizer = WaveformFeaturizer(sample_rate=sample_rate,
int_values=int_values,
augmentor=augmentor)
self.trim = trim
self.eos_id = eos_id
self.bos_id = bos_id
self.pad_id = pad_id
self.return_sample_id = return_sample_id
audio_tar_filepaths = expand_audio_filepaths(
audio_tar_filepaths=audio_tar_filepaths,
shard_strategy=shard_strategy,
world_size=world_size,
global_rank=global_rank,
)
# Put together WebDataset
self._dataset = wd.WebDataset(urls=audio_tar_filepaths,
nodesplitter=None)
if shuffle_n > 0:
self._dataset = self._dataset.shuffle(shuffle_n)
else:
logging.info(
"WebDataset will not shuffle files within the tar files.")
self._dataset = (self._dataset.rename(
audio='wav;ogg;flac',
key='__key__').to_tuple('audio', 'key').pipe(self._filter).pipe(
self._loop_offsets).map(f=self._build_sample))
def _filter(self, iterator):
"""This function is used to remove samples that have been filtered out by ASRAudioText already.
Otherwise, we would get a KeyError as _build_sample attempts to find the manifest entry for a sample
that was filtered out (e.g. for duration).
Note that if using multi-GPU training, filtering may lead to an imbalance in samples in each shard,
which may make your code hang as one process will finish before the other.
"""
class TarredAudioFilter:
def __init__(self, collection):
self.iterator = iterator
self.collection = collection
def __iter__(self):
return self
def __next__(self):
while True:
audio_bytes, audio_filename = next(self.iterator)
file_id, _ = os.path.splitext(
os.path.basename(audio_filename))
if file_id in self.collection.mapping:
return audio_bytes, audio_filename
return TarredAudioFilter(self.manifest_processor.collection)
def _loop_offsets(self, iterator):
"""This function is used to iterate through utterances with different offsets for each file.
"""
class TarredAudioLoopOffsets:
def __init__(self, collection):
self.iterator = iterator
self.collection = collection
self.current_fn = None
self.current_bytes = None
self.offset_id = 0
def __iter__(self):
return self
def __next__(self):
if self.current_fn is None:
self.current_bytes, self.current_fn = next(self.iterator)
self.offset_id = 0
else:
offset_list = self.collection.mapping[self.current_fn]
if len(offset_list) == self.offset_id + 1:
self.current_bytes, self.current_fn = next(
self.iterator)
self.offset_id = 0
else:
self.offset_id += 1
return self.current_bytes, self.current_fn, self.offset_id
return TarredAudioLoopOffsets(self.manifest_processor.collection)
def _collate_fn(self, batch):
return _speech_collate_fn(batch, self.pad_id)
def _build_sample(self, tup):
"""Builds the training sample by combining the data from the WebDataset with the manifest info.
"""
audio_bytes, audio_filename, offset_id = tup
# Grab manifest entry from self.manifest_preprocessor.collection
file_id, _ = os.path.splitext(os.path.basename(audio_filename))
manifest_idx = self.manifest_processor.collection.mapping[file_id][
offset_id]
manifest_entry = self.manifest_processor.collection[manifest_idx]
offset = manifest_entry.offset
if offset is None:
offset = 0
# Convert audio bytes to IO stream for processing (for SoundFile to read)
audio_filestream = io.BytesIO(audio_bytes)
features = self.featurizer.process(
audio_filestream,
offset=offset,
duration=manifest_entry.duration,
trim=self.trim,
orig_sr=manifest_entry.orig_sr,
)
audio_filestream.close()
# Audio features
f, fl = features, torch.tensor(features.shape[0]).long()
# Text features
t, tl = manifest_entry.text_tokens, len(manifest_entry.text_tokens)
self.manifest_processor.process_text_by_sample(sample=manifest_entry)
if self.bos_id is not None:
t = [self.bos_id] + t
tl += 1
if self.eos_id is not None:
t = t + [self.eos_id]
tl += 1
if self.return_sample_id:
return f, fl, torch.tensor(t).long(), torch.tensor(
tl).long(), manifest_idx
else:
return f, fl, torch.tensor(t).long(), torch.tensor(tl).long()
def get_manifest_sample(self, sample_id):
return self.manifest_processor.collection[sample_id]
def __iter__(self):
return self._dataset.__iter__()
def __len__(self):
return len(self.manifest_processor.collection)
def __init__(
self,
audio_tar_filepaths: Union[str, List[str]],
manifest_filepath: str,
parser: Callable,
sample_rate: int,
int_values: bool = False,
augmentor: Optional[
'nemo.collections.asr.parts.perturb.AudioAugmentor'] = None,
shuffle_n: int = 0,
min_duration: Optional[float] = None,
max_duration: Optional[float] = None,
max_utts: int = 0,
trim: bool = False,
bos_id: Optional[int] = None,
eos_id: Optional[int] = None,
pad_id: int = 0,
shard_strategy: str = "scatter",
global_rank: int = 0,
world_size: int = 0,
return_sample_id: bool = False,
):
self.manifest_processor = ASRManifestProcessor(
manifest_filepath=manifest_filepath,
parser=parser,
max_duration=max_duration,
min_duration=min_duration,
max_utts=max_utts,
bos_id=bos_id,
eos_id=eos_id,
pad_id=pad_id,
index_by_file_id=
True, # Must set this so the manifest lines can be indexed by file ID
)
self.featurizer = WaveformFeaturizer(sample_rate=sample_rate,
int_values=int_values,
augmentor=augmentor)
self.trim = trim
self.eos_id = eos_id
self.bos_id = bos_id
self.pad_id = pad_id
self.return_sample_id = return_sample_id
audio_tar_filepaths = expand_audio_filepaths(
audio_tar_filepaths=audio_tar_filepaths,
shard_strategy=shard_strategy,
world_size=world_size,
global_rank=global_rank,
)
# Put together WebDataset
self._dataset = wd.WebDataset(urls=audio_tar_filepaths,
nodesplitter=None)
if shuffle_n > 0:
self._dataset = self._dataset.shuffle(shuffle_n)
else:
logging.info(
"WebDataset will not shuffle files within the tar files.")
self._dataset = (self._dataset.rename(
audio='wav;ogg;flac',
key='__key__').to_tuple('audio', 'key').pipe(self._filter).pipe(
self._loop_offsets).map(f=self._build_sample))
def __init__(
self,
manifest_filepath: str,
sample_rate: int,
supplementary_folder: Path,
max_duration: Optional[float] = None,
min_duration: Optional[float] = None,
ignore_file: Optional[str] = None,
trim: bool = False,
n_fft=1024,
win_length=None,
hop_length=None,
window="hann",
n_mels=64,
lowfreq=0,
highfreq=None,
pitch_fmin=80,
pitch_fmax=640,
pitch_avg=0,
pitch_std=1,
tokenize_text=True,
):
"""Dataset that loads audio, log mel specs, text tokens, duration / attention priors, pitches, and energies.
Log mels, priords, pitches, and energies will be computed on the fly and saved in the supplementary_folder if
they did not exist before.
Args:
manifest_filepath (str, Path, List[str, Path]): Path(s) to the .json manifests containing information on the
dataset. Each line in the .json file should be valid json. Note: the .json file itself is not valid
json. Each line should contain the following:
"audio_filepath": <PATH_TO_WAV>
"mel_filepath": <PATH_TO_LOG_MEL_PT> (Optional)
"duration": <Duration of audio clip in seconds> (Optional)
"text": <THE_TRANSCRIPT> (Optional)
sample_rate (int): The sample rate of the audio. Or the sample rate that we will resample all files to.
supplementary_folder (Path): A folder that contains or will contain extra information such as log_mel if not
specified in the manifest .json file. It will also contain priors, pitches, and energies
max_duration (Optional[float]): Max duration of audio clips in seconds. All samples exceeding this will be
pruned prior to training. Note: Requires "duration" to be set in the manifest file. It does not load
audio to compute duration. Defaults to None which does not prune.
min_duration (Optional[float]): Min duration of audio clips in seconds. All samples lower than this will be
pruned prior to training. Note: Requires "duration" to be set in the manifest file. It does not load
audio to compute duration. Defaults to None which does not prune.
ignore_file (Optional[str, Path]): The location of a pickle-saved list of audio_ids (the stem of the audio
files) that will be pruned prior to training. Defaults to None which does not prune.
trim (Optional[bool]): Whether to apply librosa.effects.trim to the audio file. Defaults to False.
n_fft (Optional[int]): The number of fft samples. Defaults to 1024
win_length (Optional[int]): The length of the stft windows. Defaults to None which uses n_fft.
hop_length (Optional[int]): The hope length between fft computations. Defaults to None which uses n_fft//4.
window (Optional[str]): One of 'hann', 'hamming', 'blackman','bartlett', 'none'. Which corresponds to the
equivalent torch window function.
n_mels (Optional[int]): The number of mel filters. Defaults to 64.
lowfreq (Optional[int]): The lowfreq input to the mel filter calculation. Defaults to 0.
highfreq (Optional[int]): The highfreq input to the mel filter calculation. Defaults to None.
pitch_fmin (Optional[int]): The fmin input to librosa.pyin. Defaults to None.
pitch_fmax (Optional[int]): The fmax input to librosa.pyin. Defaults to None.
pitch_avg (Optional[float]): The mean that we use to normalize the pitch. Defaults to 0.
pitch_std (Optional[float]): The std that we use to normalize the pitch. Defaults to 1.
tokenize_text (Optional[bool]): Whether to tokenize (turn chars into ints). Defaults to True.
"""
super().__init__()
self.pitch_fmin = pitch_fmin
self.pitch_fmax = pitch_fmax
self.pitch_avg = pitch_avg
self.pitch_std = pitch_std
self.win_length = win_length or n_fft
self.sample_rate = sample_rate
self.hop_len = hop_length or n_fft // 4
self.parser = make_parser(name="en", do_tokenize=tokenize_text)
self.pad_id = self.parser._blank_id
Path(supplementary_folder).mkdir(parents=True, exist_ok=True)
self.supplementary_folder = supplementary_folder
audio_files = []
total_duration = 0
# Load data from manifests
# Note: audio is always required, even for text -> mel_spectrogram models, due to the fact that most models
# extract pitch from the audio
# Note: mel_filepath is not required and if not present, we then check the supplementary folder. If we fail, we
# compute the mel on the fly and save it to the supplementary folder
# Note: text is not required. Any models that require on text (spectrogram generators, end-to-end models) will
# fail if not set. However vocoders (mel -> audio) will be able to work without text
if isinstance(manifest_filepath, str):
manifest_filepath = [manifest_filepath]
for manifest_file in manifest_filepath:
with open(Path(manifest_file).expanduser(), 'r') as f:
logging.info(f"Loading dataset from {manifest_file}.")
for line in f:
item = json.loads(line)
# Grab audio, text, mel if they exist
file_info = {}
file_info["audio_filepath"] = item["audio_filepath"]
file_info["mel_filepath"] = item[
"mel_filepath"] if "mel_filepath" in item else None
file_info["duration"] = item[
"duration"] if "duration" in item else None
# Parse text
file_info["text_tokens"] = None
if "text" in item:
text = item["text"]
text_tokens = self.parser(text)
file_info["text_tokens"] = text_tokens
audio_files.append(file_info)
if file_info["duration"] is None:
logging.info(
"Not all audio files have duration information. Duration logging will be disabled."
)
total_duration = None
if total_duration is not None:
total_duration += item["duration"]
logging.info(f"Loaded dataset with {len(audio_files)} files.")
if total_duration is not None:
logging.info(f"Dataset contains {total_duration/3600:.2f} hours.")
self.data = []
if ignore_file:
logging.info(f"using {ignore_file} to prune dataset.")
with open(Path(ignore_file).expanduser(), "rb") as f:
wavs_to_ignore = set(pickle.load(f))
pruned_duration = 0 if total_duration is not None else None
pruned_items = 0
for item in audio_files:
audio_path = item['audio_filepath']
audio_id = Path(audio_path).stem
# Prune data according to min/max_duration & the ignore file
if total_duration is not None:
if (min_duration and item["duration"] < min_duration) or (
max_duration and item["duration"] > max_duration):
pruned_duration += item["duration"]
pruned_items += 1
continue
if ignore_file and (audio_id in wavs_to_ignore):
pruned_items += 1
pruned_duration += item["duration"]
wavs_to_ignore.remove(audio_id)
continue
self.data.append(item)
logging.info(
f"Pruned {pruned_items} files. Final dataset contains {len(self.data)} files"
)
if pruned_duration is not None:
logging.info(
f"Pruned {pruned_duration/3600:.2f} hours. Final dataset contains "
f"{(total_duration-pruned_duration)/3600:.2f} hours.")
self.featurizer = WaveformFeaturizer(sample_rate=sample_rate)
self.trim = trim
filterbanks = torch.tensor(librosa.filters.mel(sample_rate,
n_fft,
n_mels=n_mels,
fmin=lowfreq,
fmax=highfreq),
dtype=torch.float).unsqueeze(0)
self.fb = filterbanks
torch_windows = {
'hann': torch.hann_window,
'hamming': torch.hamming_window,
'blackman': torch.blackman_window,
'bartlett': torch.bartlett_window,
'none': None,
}
window_fn = torch_windows.get(window, None)
window_tensor = window_fn(self.win_length,
periodic=False) if window_fn else None
self.stft = lambda x: stft_patch(
input=x,
n_fft=n_fft,
hop_length=self.hop_len,
win_length=self.win_length,
window=window_tensor.to(torch.float),
)
class CharMelAudioDataset(Dataset):
@property
def output_types(self) -> Optional[Dict[str, NeuralType]]:
return {
'transcripts': NeuralType(('B', 'T'), TokenIndex()),
'transcript_length': NeuralType(('B'), LengthsType()),
'mels': NeuralType(('B', 'D', 'T'), TokenIndex()),
'mel_length': NeuralType(('B'), LengthsType()),
'audio': NeuralType(('B', 'T'), AudioSignal()),
'audio_length': NeuralType(('B'), LengthsType()),
'duration_prior': NeuralType(('B', 'T'), TokenDurationType()),
'pitches': NeuralType(('B', 'T'), RegressionValuesType()),
'energies': NeuralType(('B', 'T'), RegressionValuesType()),
}
def __init__(
self,
manifest_filepath: str,
sample_rate: int,
supplementary_folder: Path,
max_duration: Optional[float] = None,
min_duration: Optional[float] = None,
ignore_file: Optional[str] = None,
trim: bool = False,
n_fft=1024,
win_length=None,
hop_length=None,
window="hann",
n_mels=64,
lowfreq=0,
highfreq=None,
pitch_fmin=80,
pitch_fmax=640,
pitch_avg=0,
pitch_std=1,
tokenize_text=True,
):
"""Dataset that loads audio, log mel specs, text tokens, duration / attention priors, pitches, and energies.
Log mels, priords, pitches, and energies will be computed on the fly and saved in the supplementary_folder if
they did not exist before.
Args:
manifest_filepath (str, Path, List[str, Path]): Path(s) to the .json manifests containing information on the
dataset. Each line in the .json file should be valid json. Note: the .json file itself is not valid
json. Each line should contain the following:
"audio_filepath": <PATH_TO_WAV>
"mel_filepath": <PATH_TO_LOG_MEL_PT> (Optional)
"duration": <Duration of audio clip in seconds> (Optional)
"text": <THE_TRANSCRIPT> (Optional)
sample_rate (int): The sample rate of the audio. Or the sample rate that we will resample all files to.
supplementary_folder (Path): A folder that contains or will contain extra information such as log_mel if not
specified in the manifest .json file. It will also contain priors, pitches, and energies
max_duration (Optional[float]): Max duration of audio clips in seconds. All samples exceeding this will be
pruned prior to training. Note: Requires "duration" to be set in the manifest file. It does not load
audio to compute duration. Defaults to None which does not prune.
min_duration (Optional[float]): Min duration of audio clips in seconds. All samples lower than this will be
pruned prior to training. Note: Requires "duration" to be set in the manifest file. It does not load
audio to compute duration. Defaults to None which does not prune.
ignore_file (Optional[str, Path]): The location of a pickle-saved list of audio_ids (the stem of the audio
files) that will be pruned prior to training. Defaults to None which does not prune.
trim (Optional[bool]): Whether to apply librosa.effects.trim to the audio file. Defaults to False.
n_fft (Optional[int]): The number of fft samples. Defaults to 1024
win_length (Optional[int]): The length of the stft windows. Defaults to None which uses n_fft.
hop_length (Optional[int]): The hope length between fft computations. Defaults to None which uses n_fft//4.
window (Optional[str]): One of 'hann', 'hamming', 'blackman','bartlett', 'none'. Which corresponds to the
equivalent torch window function.
n_mels (Optional[int]): The number of mel filters. Defaults to 64.
lowfreq (Optional[int]): The lowfreq input to the mel filter calculation. Defaults to 0.
highfreq (Optional[int]): The highfreq input to the mel filter calculation. Defaults to None.
pitch_fmin (Optional[int]): The fmin input to librosa.pyin. Defaults to None.
pitch_fmax (Optional[int]): The fmax input to librosa.pyin. Defaults to None.
pitch_avg (Optional[float]): The mean that we use to normalize the pitch. Defaults to 0.
pitch_std (Optional[float]): The std that we use to normalize the pitch. Defaults to 1.
tokenize_text (Optional[bool]): Whether to tokenize (turn chars into ints). Defaults to True.
"""
super().__init__()
self.pitch_fmin = pitch_fmin
self.pitch_fmax = pitch_fmax
self.pitch_avg = pitch_avg
self.pitch_std = pitch_std
self.win_length = win_length or n_fft
self.sample_rate = sample_rate
self.hop_len = hop_length or n_fft // 4
self.parser = make_parser(name="en", do_tokenize=tokenize_text)
self.pad_id = self.parser._blank_id
Path(supplementary_folder).mkdir(parents=True, exist_ok=True)
self.supplementary_folder = supplementary_folder
audio_files = []
total_duration = 0
# Load data from manifests
# Note: audio is always required, even for text -> mel_spectrogram models, due to the fact that most models
# extract pitch from the audio
# Note: mel_filepath is not required and if not present, we then check the supplementary folder. If we fail, we
# compute the mel on the fly and save it to the supplementary folder
# Note: text is not required. Any models that require on text (spectrogram generators, end-to-end models) will
# fail if not set. However vocoders (mel -> audio) will be able to work without text
if isinstance(manifest_filepath, str):
manifest_filepath = [manifest_filepath]
for manifest_file in manifest_filepath:
with open(Path(manifest_file).expanduser(), 'r') as f:
logging.info(f"Loading dataset from {manifest_file}.")
for line in f:
item = json.loads(line)
# Grab audio, text, mel if they exist
file_info = {}
file_info["audio_filepath"] = item["audio_filepath"]
file_info["mel_filepath"] = item[
"mel_filepath"] if "mel_filepath" in item else None
file_info["duration"] = item[
"duration"] if "duration" in item else None
# Parse text
file_info["text_tokens"] = None
if "text" in item:
text = item["text"]
text_tokens = self.parser(text)
file_info["text_tokens"] = text_tokens
audio_files.append(file_info)
if file_info["duration"] is None:
logging.info(
"Not all audio files have duration information. Duration logging will be disabled."
)
total_duration = None
if total_duration is not None:
total_duration += item["duration"]
logging.info(f"Loaded dataset with {len(audio_files)} files.")
if total_duration is not None:
logging.info(f"Dataset contains {total_duration/3600:.2f} hours.")
self.data = []
if ignore_file:
logging.info(f"using {ignore_file} to prune dataset.")
with open(Path(ignore_file).expanduser(), "rb") as f:
wavs_to_ignore = set(pickle.load(f))
pruned_duration = 0 if total_duration is not None else None
pruned_items = 0
for item in audio_files:
audio_path = item['audio_filepath']
audio_id = Path(audio_path).stem
# Prune data according to min/max_duration & the ignore file
if total_duration is not None:
if (min_duration and item["duration"] < min_duration) or (
max_duration and item["duration"] > max_duration):
pruned_duration += item["duration"]
pruned_items += 1
continue
if ignore_file and (audio_id in wavs_to_ignore):
pruned_items += 1
pruned_duration += item["duration"]
wavs_to_ignore.remove(audio_id)
continue
self.data.append(item)
logging.info(
f"Pruned {pruned_items} files. Final dataset contains {len(self.data)} files"
)
if pruned_duration is not None:
logging.info(
f"Pruned {pruned_duration/3600:.2f} hours. Final dataset contains "
f"{(total_duration-pruned_duration)/3600:.2f} hours.")
self.featurizer = WaveformFeaturizer(sample_rate=sample_rate)
self.trim = trim
filterbanks = torch.tensor(librosa.filters.mel(sample_rate,
n_fft,
n_mels=n_mels,
fmin=lowfreq,
fmax=highfreq),
dtype=torch.float).unsqueeze(0)
self.fb = filterbanks
torch_windows = {
'hann': torch.hann_window,
'hamming': torch.hamming_window,
'blackman': torch.blackman_window,
'bartlett': torch.bartlett_window,
'none': None,
}
window_fn = torch_windows.get(window, None)
window_tensor = window_fn(self.win_length,
periodic=False) if window_fn else None
self.stft = lambda x: stft_patch(
input=x,
n_fft=n_fft,
hop_length=self.hop_len,
win_length=self.win_length,
window=window_tensor.to(torch.float),
)
def __getitem__(self, index):
spec = None
sample = self.data[index]
features = self.featurizer.process(sample["audio_filepath"],
trim=self.trim)
audio, audio_length = features, torch.tensor(features.shape[0]).long()
if isinstance(sample["text_tokens"], str):
# If tokenize_text is False for Phone dataset
text = sample["text_tokens"]
text_length = None
else:
text = torch.tensor(sample["text_tokens"]).long()
text_length = torch.tensor(len(sample["text_tokens"])).long()
audio_stem = Path(sample["audio_filepath"]).stem
# Load mel if it exists
mel_path = sample["mel_filepath"]
if mel_path and Path(mel_path).exists():
log_mel = torch.load(mel_path)
else:
mel_path = Path(self.supplementary_folder) / f"mel_{audio_stem}.pt"
if mel_path.exists():
log_mel = torch.load(mel_path)
else:
# disable autocast to get full range of stft values
with torch.cuda.amp.autocast(enabled=False):
spec = self.stft(audio)
# guard is needed for sqrt if grads are passed through
guard = CONSTANT # TODO: Enable 0 if not self.use_grads else CONSTANT
if spec.dtype in [torch.cfloat, torch.cdouble]:
spec = torch.view_as_real(spec)
spec = torch.sqrt(spec.pow(2).sum(-1) + guard)
mel = torch.matmul(self.fb.to(spec.dtype), spec)
log_mel = torch.log(
torch.clamp(mel, min=torch.finfo(mel.dtype).tiny))
torch.save(log_mel, mel_path)
log_mel = log_mel.squeeze(0)
log_mel_length = torch.tensor(log_mel.shape[1]).long()
duration_prior = None
if text_length is not None:
### Make duration attention prior if not exist in the supplementary folder
prior_path = Path(self.supplementary_folder
) / f"pr_tl{text_length}_al_{log_mel_length}.pt"
if prior_path.exists():
duration_prior = torch.load(prior_path)
else:
duration_prior = beta_binomial_prior_distribution(
text_length, log_mel_length)
duration_prior = torch.from_numpy(duration_prior)
torch.save(duration_prior, prior_path)
# Load pitch file (F0s)
pitch_path = (
Path(self.supplementary_folder) /
f"{audio_stem}_pitch_pyin_fmin{self.pitch_fmin}_fmax{self.pitch_fmax}_fl{self.win_length}_hs{self.hop_len}.pt"
)
if pitch_path.exists():
pitch = torch.load(pitch_path)
else:
pitch, _, _ = librosa.pyin(
audio.numpy(),
fmin=self.pitch_fmin,
fmax=self.pitch_fmax,
frame_length=self.win_length,
sr=self.sample_rate,
fill_na=0.0,
)
pitch = torch.from_numpy(pitch)
torch.save(pitch, pitch_path)
# Standize pitch
pitch -= self.pitch_avg
pitch[pitch == -self.
pitch_avg] = 0.0 # Zero out values that were perviously zero
pitch /= self.pitch_std
# Load energy file (L2-norm of the amplitude of each STFT frame of an utterance)
energy_path = Path(
self.supplementary_folder
) / f"{audio_stem}_energy_wl{self.win_length}_hs{self.hop_len}.pt"
if energy_path.exists():
energy = torch.load(energy_path)
else:
if spec is None:
spec = self.stft(audio)
energy = torch.linalg.norm(spec.squeeze(0), axis=0)
# Save to new file
torch.save(energy, energy_path)
return text, text_length, log_mel, log_mel_length, audio, audio_length, duration_prior, pitch, energy
def __len__(self):
return len(self.data)
def _collate_fn(self, batch):
log_mel_pad = torch.finfo(batch[0][2].dtype).tiny
_, tokens_lengths, _, log_mel_lengths, _, audio_lengths, duration_priors_list, pitches, energies = zip(
*batch)
max_tokens_len = max(tokens_lengths).item()
max_log_mel_len = max(log_mel_lengths)
max_audio_len = max(audio_lengths).item()
max_pitches_len = max([len(i) for i in pitches])
max_energies_len = max([len(i) for i in energies])
if max_pitches_len != max_energies_len or max_pitches_len != max_log_mel_len:
logging.warning(
f"max_pitches_len: {max_pitches_len} != max_energies_len: {max_energies_len} != "
f"max_mel_len:{max_log_mel_len}. Your training run will error out!"
)
# Define empty lists to be batched
duration_priors = torch.zeros(
len(duration_priors_list),
max([prior_i.shape[0] for prior_i in duration_priors_list]),
max([prior_i.shape[1] for prior_i in duration_priors_list]),
)
audios, tokens, log_mels, pitches, energies = [], [], [], [], []
for i, sample_tuple in enumerate(batch):
token, token_len, log_mel, log_mel_len, audio, audio_len, duration_prior, pitch, energy = sample_tuple
# Pad text tokens
token_len = token_len.item()
if token_len < max_tokens_len:
pad = (0, max_tokens_len - token_len)
token = torch.nn.functional.pad(token, pad, value=self.pad_id)
tokens.append(token)
# Pad mel
log_mel_len = log_mel_len
if log_mel_len < max_log_mel_len:
pad = (0, max_log_mel_len - log_mel_len)
log_mel = torch.nn.functional.pad(log_mel,
pad,
value=log_mel_pad)
log_mels.append(log_mel)
# Pad audio
audio_len = audio_len.item()
if audio_len < max_audio_len:
pad = (0, max_audio_len - audio_len)
audio = torch.nn.functional.pad(audio, pad)
audios.append(audio)
# Pad duration_prior
duration_priors[i, :duration_prior.shape[0], :duration_prior.
shape[1]] = duration_prior
# Pad pitch
if len(pitch) < max_pitches_len:
pad = (0, max_pitches_len - len(pitch))
pitch = torch.nn.functional.pad(pitch, pad)
pitches.append(pitch)
# Pad energy
if len(energy) < max_energies_len:
pad = (0, max_energies_len - len(energy))
energy = torch.nn.functional.pad(energy, pad)
energies.append(energy)
audios = torch.stack(audios)
audio_lengths = torch.stack(audio_lengths)
tokens = torch.stack(tokens)
tokens_lengths = torch.stack(tokens_lengths)
log_mels = torch.stack(log_mels)
log_mel_lengths = torch.stack(log_mel_lengths)
pitches = torch.stack(pitches)
energies = torch.stack(energies)
logging.debug(f"audios: {audios.shape}")
logging.debug(f"audio_lengths: {audio_lengths.shape}")
logging.debug(f"tokens: {tokens.shape}")
logging.debug(f"tokens_lengths: {tokens_lengths.shape}")
logging.debug(f"log_mels: {log_mels.shape}")
logging.debug(f"log_mel_lengths: {log_mel_lengths.shape}")
logging.debug(f"duration_priors: {duration_priors.shape}")
logging.debug(f"pitches: {pitches.shape}")
logging.debug(f"energies: {energies.shape}")
return (tokens, tokens_lengths, log_mels, log_mel_lengths,
duration_priors, pitches, energies)
def decode(self, tokens):
assert len(tokens.squeeze().shape) in [0, 1]
return self.parser.decode(tokens)