def main():
config = ConfigXT()
load = FileXT(config.audio_path)
print(
colored('Preprocessing audio for ', 'blue', attrs=['bold']) +
load.basename)
data = preprocess.preprocess(load.filename,
config.speaker,
config,
verbose=False)
if config.infer_acoustic:
f0, rmse = acoustic_infer(data, config)
for i in range(len(data)):
data[i][4] = f0[i]
data[i][5] = rmse[i]
mel = durian_infer(data, config)
if config.vocoder == 'wavernn':
wave = wavernn_infer(mel, config)
elif config.vocoder == 'waveglow':
wave = waveglow_infer(mel, config)
savename = config.durian_path.replace('.pt', '_') + FileXT(
config.vocoder_path).basestem + '_speaker' + str(
config.speaker) + '_' + load.basename
torchaudio.save(savename, wave, config.sample_rate)
print(
colored('Audio generated to ', 'blue', attrs=['bold']) + savename +
'\n')
def test_vae(test_fns):
"""
Used with a trained model, where MODEL_FN is already a saved .pth file
"""
d = prep(test_fns)
y_hats = []
length = len(d['data'])
img_y_hats = []
for epoch in range(1, EPOCHS + 1):
print(f'test epoch: {epoch}')
train.train_epoch(d, epoch, BATCH_SIZE, device)
apply_idx = random.randint(0, length)
sample = d['data'][apply_idx].view(BATCH_SIZE, 2, -1)
y_hat = utilz.gen_apply(d['m'], sample, device).cpu()
print(f'y_hat.shape: {y_hat.shape}')
img_vers = y_hat.view(1, 3, 240, 245) # for 2 seconds
img_y_hats.append(img_vers)
y_hats.append(y_hat)
song = torch.cat(y_hats, dim=1)
print(song)
if SAVE_SONG:
save_wavfn = f'vaeconv_{RUN_TIME}.wav'
song_path = d['path'] + save_wavfn
torchaudio.save(song_path, song, d['sr'])
print(f'audio saved to {song_path}')
return song# , video
def test_filter():
audio, sr = torchaudio.load("tests/classical.00002.wav")
num_samples = sr * 5
transform = Compose([HighLowPass(sample_rate=sr)], )
audios = transform(audio)
torchaudio.save("tests/filter.wav", audios, sample_rate=sr)
assert audios.shape[1] == audio.shape[1]
def infer_wavenet(args):
import sys
sys.path.append('thirdparty/wavenet_vocoder')
from train import build_model
from synthesis import wavegen
from tqdm import tqdm
target_sample_rate = 22050
hparams, model = load_model(args.model_name)
meller = MelSpectrogram()
files = [
item for item in os.listdir(args.folder_in) if item.endswith('wav')
]
for idx, audio in enumerate(files):
wav_path = os.path.join(args.folder_in, audio)
wav = load_wav(wav_path, target_sample_rate)
c = meller(wav)[0]
if c.shape[1] != hparams.num_mels:
c = c.transpose(0, 1)
# Range [0, 4] was used for training Tacotron2 but WaveNet vocoder assumes [0, 1]
# c = np.interp(c, (0, 4), (0, 1))
# Generate
waveform = wavegen(model, c=c, fast=True, tqdm=tqdm)
path = os.path.join(args.folder_out, audio)
folder = os.path.dirname(path)
if not os.path.exists(folder):
os.makedirs(folder)
torchaudio.save(path, waveform, hparams.sample_rate)
def main(args):
# spectrogram = torch.from_numpy(np.load(args.spectrogram_path))
params = {}
if args.noise_schedule:
params['noise_schedule'] = torch.from_numpy(
np.load(args.noise_schedule))
start_time = time.time()
model = load_model(model_dir=args.model_dir, params=params)
if not os.path.exists(args.output_path):
os.mkdir(args.output_path)
time_consuming = time.time() - start_time
print(" > Load model, time consuming {}s".format(round(time_consuming, 2)))
mels = os.listdir(args.spectrogram_path)
for mel_file in mels:
if not os.path.isdir(mel_file) and mel_file[-2:] == "pt":
start_time = time.time()
print(" > Start inferencing sentence {} . ".format(mel_file))
spectrogram = torch.tensor(
torch.load(os.path.join(args.spectrogram_path, mel_file)))
audio, sr = predict(spectrogram, model)
wav_name = os.path.join(args.output_path,
mel_file[:-7] + "_wg.wav")
torchaudio.save(wav_name, audio.cpu(), sample_rate=sr)
time_consuming = time.time() - start_time
print(" > Complete, time consuming {}s".format(
round(time_consuming, 2)))
def inference_fast(mels, real_audio=None, i=0, epoch=0):
generator.eval()
val_losses = []
audio = torch.zeros(mels.shape[0], 1, 1).to(device)
with torch.no_grad():
for length in range(1, mels.shape[2] + 1):
res = generator(audio[:, :, -1:],
mels[:, :, length - 1:length],
fast_generation=True)
audio = torch.cat([
audio,
mu_decode(torch.argmax(res[:, :, -1:], dim=1)).unsqueeze(1)
],
dim=2)
name = "example " + str(i) + "_no_tf"
torchaudio.save("gen.wav",
audio[0].squeeze().detach().cpu().numpy(),
sample_rate=22050)
wandb_gen = wandb.Audio(audio[0].squeeze().detach().cpu().numpy(),
caption="Inference_1",
sample_rate=22050)
wandb_audios = [wandb_gen]
if real_audio != None:
wandb_real = wandb.Audio(
real_audio[0].squeeze().detach().cpu().numpy(),
caption="Real_1",
sample_rate=22050)
wandb_audios.append(wandb_real)
wandb.log({name: wandb_audios}, step=epoch)
def resample_folder(in_dir, out_dir, target_fs=16000, regex="*.wav"):
"""
Resamples the audio files contained in the in_dir folder and saves them in out_dir folder
Args:
in_dir (str): path to audio directory (audio to be resampled)
out_dir (str): path to audio resampled directory
target_fs (int, optional): target sample rate. Defaults to 16000.
regex (str, optional): regular expression for extension of file. Defaults to "*.wav".
"""
compute = True
files = glob.glob(os.path.join(in_dir, regex))
if os.path.exists(out_dir):
out_files = glob.glob(os.path.join(out_dir, regex))
if len(files) == len(out_files):
compute = False
if compute:
for f in tqdm.tqdm(files):
audio, orig_fs = torchaudio.load(f)
audio = resample(audio, orig_fs, target_fs)
os.makedirs(
Path(os.path.join(out_dir,
Path(f).relative_to(Path(in_dir)))).parent,
exist_ok=True,
)
torchaudio.save(
os.path.join(out_dir,
Path(f).relative_to(Path(in_dir))),
audio,
target_fs,
)
return compute
def asadsa2():
""" """
from draugr.torch_utilities import to_tensor
from neodroidaudition.data.recognition.libri_speech import LibriSpeech
from neodroidaudition.noise_generation.gaussian_noise import white_noise
import torchaudio
from pathlib import Path
libri_speech = LibriSpeech(path=Path.home() / "Data" / "Audio" /
"Speech" / "LibriSpeech")
files, sr = zip(*[(v[0].numpy(), v[1])
for _, v in zip(range(1), libri_speech)])
assert all([sr[0] == s for s in sr[1:]])
normed = files[0]
mixed = mix_ratio(normed, normed, 0)
mixed2 = mix_ratio(mixed, mixed, 0)
print(normed, mixed)
print(mixed2, mixed)
print(root_mean_square(normed))
print(root_mean_square(mixed))
print(root_mean_square(mixed2))
assert numpy.allclose(normed, mixed)
assert numpy.allclose(mixed2, mixed)
torchaudio.save(
str(ensure_existence(Path.cwd() / "exclude") / "mixed_same.wav"),
to_tensor(mixed),
int(sr[0]),
)
def convert(path):
split_list = []
with open(join(path, 'hub5e_00.pem'), 'r') as fp:
for line in fp:
if line[0] not in ['e', 's']: continue
if line[-1] == '\n': line = line[:-1]
line = line.split(' ')
name = line[0] + '-' + line[1] + '.wav'
time_0 = float(line[3])
i = 4
while len(line[i]) <= 1:
i += 1
time_1 = float(line[i])
split_list.append((name, time_0, time_1))
print('From {} found {} segments'.format(join(path, 'hub5e_00.pem'),
len(split_list)))
split_list = sorted(split_list, key=lambda x: x[0])
file_list = list(Path(path).rglob('*.wav'))
file_list = sorted(file_list)
print('From {} found {} wav files.'.format(path, len(file_list)))
j = 0
for idx, p in tqdm(enumerate(file_list)):
waveform, sr = torchaudio.load(str(p))
count = 0
while split_list[j][0] == str(p).split('/')[-1]:
t_0, t_1 = split_list[j][1], split_list[j][2]
split_wavform = waveform[:, int(t_0 * sr):int(t_1 * sr) + 1]
torchaudio.save(
str(p)[:-4] + '-' + str(count).zfill(3) + '.wav',
split_wavform, sr)
j += 1
count += 1
if j == len(split_list): break
print('Finished splitting {} wav files into {}.'.format(
j, len(split_list)))
def save_audio(wav,
path,
samplerate,
bitrate=320,
clip='rescale',
bits_per_sample=16,
as_float=False):
"""Save audio file, automatically preventing clipping if necessary
based on the given `clip` strategy. If the path ends in `.mp3`, this
will save as mp3 with the given `bitrate`.
"""
wav = prevent_clip(wav, mode=clip)
path = Path(path)
suffix = path.suffix.lower()
if suffix == ".mp3":
encode_mp3(wav, path, samplerate, bitrate)
elif suffix == ".wav":
if as_float:
bits_per_sample = 32
encoding = 'PCM_F'
else:
encoding = 'PCM_S'
ta.save(str(path),
wav,
sample_rate=samplerate,
encoding=encoding,
bits_per_sample=bits_per_sample)
else:
raise ValueError(f"Invalid suffix for path: {suffix}")
def test_cut_load_custom_recording_pad_left():
sampling_rate = 16000
duration = 52.4
audio = np.random.randn(1, compute_num_samples(
duration, sampling_rate)).astype(np.float32)
audio /= np.abs(audio).max() # normalize to [-1, 1]
with NamedTemporaryFile(suffix=".wav") as f:
torchaudio.save(f.name, torch.from_numpy(audio), sampling_rate)
f.flush()
os.fsync(f)
recording = Recording.from_file(f.name)
# Note: MonoCut doesn't normally have an "alignment" attribute,
# and a "load_alignment()" method.
# We are dynamically extending it.
cut = MonoCut(
id="x",
start=0,
duration=duration,
channel=0,
recording=dummy_recording(0, duration=duration),
)
cut.my_favorite_song = recording
cut_pad = cut.pad(duration=60.0, direction="left")
restored_audio = cut_pad.load_my_favorite_song()
assert restored_audio.shape == (1, 960000) # 16000 * 60
np.testing.assert_almost_equal(0, restored_audio[:, :-audio.shape[1]])
np.testing.assert_almost_equal(audio, restored_audio[:,
-audio.shape[1]:])
def main(cfg: DictConfig):
spectrogram_path = Path(
hydra.utils.to_absolute_path(cfg.inference.spectrogram_path))
output_path = Path(
hydra.utils.to_absolute_path(cfg.inference.inferenced_path))
output_audio_path = output_path / (spectrogram_path.stem + ".wav")
output_plot_path = output_path / (spectrogram_path.stem + ".png")
model = core.model.WaveNet.load_from_checkpoint(
hydra.utils.to_absolute_path(cfg.inference.checkpoint_path))
model = model.eval().to(cfg.inference.device)
spectrogram = torch.load(spectrogram_path).to(model.device)
if len(spectrogram.shape) < 3:
spectrogram = spectrogram.unsqueeze(0)
if cfg.inference.cut_size is not None:
spectrogram = spectrogram[:, :, :int(cfg.inference.cut_size *
cfg.data.sample_rate /
cfg.preprocessing.hop_length)]
audio = model.inference(spectrogram).squeeze(0)
audio = torchaudio.functional.mu_law_decoding(audio, model.n_mu_law)
torchaudio.save(str(output_audio_path),
audio.detach().cpu(),
sample_rate=cfg.data.sample_rate)
plt.plot(audio[0].detach().cpu().numpy())
plt.savefig(output_plot_path)
def forward(self, input_path: str, output_path: str):
torchaudio.sox_effects.init_sox_effects()
# 1. load audio
waveform, sample_rate = torchaudio.load(input_path)
# 2. Add background noise
alpha = 0.01
waveform = alpha * torch.randn_like(waveform) + (1 - alpha) * waveform
# 3. Reample the RIR filter to much the audio sample rate
rir, _ = torchaudio.sox_effects.apply_effects_tensor(
self.rir,
self.rir_sample_rate,
effects=[["rate", str(sample_rate)]])
rir = rir / torch.norm(rir, p=2)
rir = torch.flip(rir, [1])
# 4. Apply RIR filter
waveform = torch.nn.functional.pad(waveform, (rir.shape[1] - 1, 0))
waveform = torch.nn.functional.conv1d(waveform[None, ...], rir[None,
...])[0]
# Save
torchaudio.save(output_path, waveform, sample_rate)
def _test_1_save_sine(self):
# save created file
sinewave_filepath = os.path.join(self.test_dirpath, "assets",
"sinewave.wav")
sr = 16000
freq = 440
volume = 0.3
y = (torch.cos(
2 * math.pi * torch.arange(0, 4 * sr).float() * freq / sr))
y.unsqueeze_(0)
# y is between -1 and 1, so must scale
y = (y * volume * (2**31)).long()
torchaudio.save(sinewave_filepath, y, sr)
self.assertTrue(os.path.isfile(sinewave_filepath))
# test precision
new_precision = 32
new_filepath = os.path.join(self.test_dirpath, "test.wav")
si, ei = torchaudio.info(sinewave_filepath)
torchaudio.save(new_filepath, y, sr, new_precision)
si32, ei32 = torchaudio.info(new_filepath)
self.assertEqual(si.precision, 16)
self.assertEqual(si32.precision, new_precision)
os.unlink(new_filepath)
def generate_background_noise(speech_commands):
"""Split the background noise provided by the dataset in 1 second chunks.
Parameters:
speech_commands (torch.utils.data.Dataset): Speech Command dataset as defined by torchaudio.
"""
background_noise = glob.glob(
os.path.join(speech_commands._path, "_background_noise_", "*.wav"))
os.makedirs(os.path.join(speech_commands._path, "background"),
exist_ok=True)
for file in background_noise:
waveform, sample_rate = torchaudio.load(file)
background_waveforms = torch.split(waveform, sample_rate, dim=1)[:-1]
for idx, background_waveform in enumerate(background_waveforms):
torchaudio.save(
os.path.join(
speech_commands._path,
"background",
f"{hash(waveform)}_nohash_{idx}.wav",
),
background_waveform,
sample_rate=sample_rate,
)
def run_inference(text, audio=None):
generator.eval()
text = [ord(c) for c in text if ord(c) < 256]
text = torch.tensor(text).view(1, -1)
with torch.no_grad():
text = text.to(device)
pad_mask = (text != 0).to(device)
res, before_prenet, stop_token, attn_matrix = generator(
text, pad_mask, None, device)
wandb_gen = wandb.Image(res[0, :, :].detach().cpu().numpy(),
caption="Generated")
wandb_attn = wandb.Image(256 *
attn_matrix[0, :, :].detach().cpu().numpy(),
caption="Attention")
wandb_images = [wandb_gen, wandb_attn]
audio_gen = vocoder.inference(res[:1, :, :].detach().cpu())
torchaudio.save("gen.wav", audio_gen, sample_rate=22050)
wandb_audios = [
wandb.Audio("gen.wav", caption="Generated", sample_rate=22050)
]
if audio != None:
wandb_real = wandb.Image(audio[0, :, :].detach().cpu().numpy(),
caption="Real")
wandb_images.append(wandb_real)
audio_real = vocoder.inference(audio[:1, :, :])
torchaudio.save("temp_real.wav", audio_real, sample_rate=22050)
wandb_audios.append(
wandb.Audio("temp_real.wav", caption="Real",
sample_rate=22050))
wandb.log({"mels": wandb_images}, step=0)
wandb.log({"audios": wandb_audios}, step=0)
api.flush()
def _test_4_load_partial(self):
num_frames = 101
offset = 201
# load entire mono sinewave wav file, load a partial copy and then compare
input_sine_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav')
x_sine_full, sr_sine = torchaudio.load(input_sine_path)
x_sine_part, _ = torchaudio.load(input_sine_path, num_frames=num_frames, offset=offset)
l1_error = x_sine_full[:, offset:(num_frames + offset)].sub(x_sine_part).abs().sum().item()
# test for the correct number of samples and that the correct portion was loaded
self.assertEqual(x_sine_part.size(1), num_frames)
self.assertEqual(l1_error, 0.)
# create a two channel version of this wavefile
x_2ch_sine = x_sine_full.repeat(1, 2)
out_2ch_sine_path = os.path.join(self.test_dirpath, 'assets', '2ch_sinewave.wav')
torchaudio.save(out_2ch_sine_path, x_2ch_sine, sr_sine)
x_2ch_sine_load, _ = torchaudio.load(out_2ch_sine_path, num_frames=num_frames, offset=offset)
os.unlink(out_2ch_sine_path)
l1_error = x_2ch_sine_load.sub(x_2ch_sine[:, offset:(offset + num_frames)]).abs().sum().item()
self.assertEqual(l1_error, 0.)
# test with two channel mp3
x_2ch_full, sr_2ch = torchaudio.load(self.test_filepath, normalization=True)
x_2ch_part, _ = torchaudio.load(self.test_filepath, normalization=True, num_frames=num_frames, offset=offset)
l1_error = x_2ch_full[:, offset:(offset + num_frames)].sub(x_2ch_part).abs().sum().item()
self.assertEqual(x_2ch_part.size(1), num_frames)
self.assertEqual(l1_error, 0.)
# check behavior if number of samples would exceed file length
offset_ns = 300
x_ns, _ = torchaudio.load(input_sine_path, num_frames=100000, offset=offset_ns)
self.assertEqual(x_ns.size(1), x_sine_full.size(1) - offset_ns)
# check when offset is beyond the end of the file
with self.assertRaises(RuntimeError):
torchaudio.load(input_sine_path, offset=100000)
def extract_wav_files(data_dir, params_list, clip_format, sample_rate, output_dir):
clip_dir = 'wavs'
clip_ext = clip_format
os.makedirs(os.path.join(output_dir, clip_dir), exist_ok=True)
max_int16 = torch.iinfo(torch.int16).max
for params in params_list:
id_ = params['id']
metadata_file = os.path.join('data', f'{id_}.metadata.txt')
audio_dir = os.path.join('data', f'{id_}')
with open(metadata_file, 'rt') as metadata_f:
current_file = None
current_audio = None
for line in metadata_f:
parts = line.rstrip('\r\n').split('|')
id_, audio_file, audio_start, audio_end, _, _ = parts
audio_start, audio_end = int(audio_start), int(audio_end)
if current_file != audio_file:
file = os.path.join(audio_dir, audio_file)
print(f'\rReading {file}', end='')
y, sr = torchaudio.load(file)
assert len(y.shape) == 2 and y.shape[0] == 1
assert y.dtype == torch.float32
assert sr == sample_rate
y = (y * max_int16 / torch.max(torch.abs(y))).to(torch.int16)
current_file = audio_file
current_audio = y
output_file = os.path.join(output_dir, clip_dir, f'{id_}.{clip_ext}')
y = current_audio[:, audio_start:audio_end]
torchaudio.save(output_file, y, sample_rate)
def predict_from_mel(ctx: Context, squeeze_wave_checkpoint: str,
output_dir: str):
config: Config = ctx.obj["CONFIG"]
output_dir = Path(output_dir)
output_dir.mkdir(exist_ok=True, parents=True)
on_gpu = torch.cuda.is_available()
squeeze_wave = LitSqueezeWave.load_from_checkpoint(squeeze_wave_checkpoint,
config=config,
on_gpu=False)
squeeze_wave = SqueezeWave.remove_norms(squeeze_wave.model)
squeeze_wave = squeeze_wave.eval()
trump_spec = torch.load('data/preprocessed-tacotron2/mel/speech00_0000.pt')
lj_spec = torch.load('data/lj-speech-tacotron2/mel/LJ001-0001.pt')
prefix = str(Path(squeeze_wave_checkpoint).name)
for spec, suffix in zip([trump_spec, lj_spec], ["trump", "lj"]):
audio = squeeze_wave.infer(spec)
audio_path = output_dir / f"{prefix}-{suffix}.wav"
torchaudio.save(str(audio_path),
audio.cpu(),
sample_rate=config.dataset.audio_format.sampling_rate)
print(f"Results saved to {output_dir}")
def _preprocess_files(self) -> None:
"""
Segment the waveform files based on :py:attr:`segment_len` and cache the file-segments.
"""
if self.segment_len is None:
return
processed_audio_dir = self.base_dir / f"segment_len={self.segment_len}"
processed_audio_dir.mkdir(parents=True, exist_ok=True)
waveform_paths = self.base_dir / self.metadata[
"filePath"] # type: ignore
segment_filenames: List[Tuple[str, str]] = []
for path in tqdm(waveform_paths,
desc="Preprocessing",
colour=self._PBAR_COL):
waveform_filename = path.stem
waveform, sr = torchaudio.load(path) # type: ignore
audio_len = waveform.size(-1) / sr
frac_remainder, num_segments = math.modf(audio_len /
self.segment_len)
num_segments = int(num_segments)
if frac_remainder >= 0.5:
self.logger.debug(
f"Length of audio file '{path.resolve()}' is not integer-divisible by "
f"{self.segment_len}: terminally zero-padding the file along the "
f"time-axis to compensate.", )
padding = torch.zeros(
waveform.size(0),
int((self.segment_len -
(frac_remainder * self.segment_len)) * sr),
)
waveform = torch.cat((waveform, padding), dim=-1)
num_segments += 1
if 0 < frac_remainder < 0.5:
self.logger.debug(
f"Length of audio file '{path.resolve()}' is not integer-divisible by "
f"{self.segment_len} and not of sufficient length to be padded "
f"(fractional remainder must be greater than 0.5): discarding terminal segment.",
)
waveform = waveform[:, :int(num_segments * self.segment_len *
sr)]
waveform_segments = waveform.chunk(chunks=num_segments, dim=-1)
for seg_idx, segment in enumerate(waveform_segments):
segment_filename = f"{waveform_filename}_{seg_idx}.wav"
segment_filepath = processed_audio_dir / segment_filename
torchaudio.save( # type: ignore
filepath=segment_filepath,
src=segment,
sample_rate=sr,
)
segment_filenames.append(
(waveform_filename,
str(segment_filepath.relative_to(self.base_dir))))
pd.DataFrame(segment_filenames,
columns=["fileName", "filePath"
]).to_csv(processed_audio_dir / "filepaths.csv",
index=False)
def main():
args = get_args()
# Load in model from checkpoint
model = load_model("demucs_quantized").to(args.device)
# Initialize output path
out = Path("separated") / "demucs_quantized"
out.mkdir(parents=True, exist_ok=True)
print(f"Separated tracks will be stored in {out.resolve()}")
for track in args.tracks:
if not track.exists():
print(f"File {track} does not exist.")
continue
print(f"Separating track {track}")
# Load audio and run_model
wav, sr = torchaudio.load(str(track))
wav = wav.to(args.device)
wav, ref = preprocess_and_normalize_audio(wav, sr,
model.audio_channels,
model.samplerate)
sources = run_model_with_splits_and_shifts(model, wav, split=True)
sources = sources * ref.std() + ref.mean()
# Save outputs
track_folder = out / track.name.rsplit(".", 1)[0]
track_folder.mkdir(exist_ok=True)
for source, name in zip(sources, model.sources):
source = source / max(1.01 * source.abs().max(), 1)
source = source.cpu()
wavname = str(track_folder / f"{name}.wav")
torchaudio.save(wavname, source, sample_rate=model.samplerate)
def resample_folder(input_folder, output_folder, fs, regex):
files = get_all_files(input_folder, match_and=[regex])
torchaudio.initialize_sox()
for f in tqdm.tqdm(files):
# we use sox because torchaudio.Resample uses too much RAM.
resample = torchaudio.sox_effects.SoxEffectsChain()
resample.append_effect_to_chain("rate", [fs])
resample.set_input_file(f)
audio, fs = resample.sox_build_flow_effects()
audio = (audio / torch.max(torch.abs(audio), dim=-1, keepdim=True)[0]
) # scale back otherwise you get empty .wav file
os.makedirs(
Path(
os.path.join(output_folder,
Path(f).relative_to(Path(input_folder)))).parent,
exist_ok=True,
)
torchaudio.save(
os.path.join(output_folder,
Path(f).relative_to(Path(input_folder))),
audio,
fs,
)
torchaudio.shutdown_sox()
def compute_objectives(self, predict_wavs, batch, stage):
"""Computes the loss given the predicted and targeted outputs"""
clean_wavs, lens = batch.clean_sig
loss = self.hparams.compute_cost(predict_wavs, clean_wavs, lens)
self.loss_metric.append(
batch.id, predict_wavs, clean_wavs, lens, reduction="batch"
)
if stage != sb.Stage.TRAIN:
# Evaluate speech quality/intelligibility
self.stoi_metric.append(
batch.id, predict_wavs, clean_wavs, lens, reduction="batch"
)
self.pesq_metric.append(
batch.id, predict=predict_wavs, target=clean_wavs, lengths=lens
)
# Write wavs to file
if stage == sb.Stage.TEST:
lens = lens * clean_wavs.shape[1]
for name, pred_wav, length in zip(batch.id, predict_wavs, lens):
name += ".wav"
enhance_path = os.path.join(
self.hparams.enhanced_folder, name
)
pred_wav = pred_wav / torch.max(torch.abs(pred_wav)) * 0.99
torchaudio.save(
enhance_path,
torch.unsqueeze(pred_wav[: int(length)].cpu(), 0),
16000,
)
return loss
def write_audio(filepath, audio, samplerate):
"""write audio on disk. It is basically a wrapper to support saving
audio signals in the speechbrain format (audio, channels).
Arguments
----------
filepath: path
Path where to save the audio file
audio : torch.Tensor
Audio file in the expected speechbrain format (signal, channels)
samplerate: int
Sample rate (e.g, 16000)
Example
-------
>>> import os
>>> tmpfile = os.path.join(str(getfixture('tmpdir')), "wave.wav")
>>> dummywav = torch.rand(16000, 2)
>>> write_audio(tmpfile, dummywav, 16000)
>>> loaded = read_audio(tmpfile)
>>> loaded.allclose(dummywav,atol=1e-4) # replace with eq with sox_io backend
True
"""
if len(audio.shape) == 2:
audio = audio.transpose(0, 1)
elif len(audio.shape) == 1:
audio = audio.unsqueeze(0)
torchaudio.save(filepath, audio, samplerate)
def test_audio_caching_disabled_works():
lhotse.set_caching_enabled(False) # Disable caching.
np.random.seed(89) # Reproducibility.
# Prepare two different waveforms.
noise1 = np.random.rand(1, 32000).astype(np.float32)
noise2 = np.random.rand(1, 32000).astype(np.float32)
# Sanity check -- the noises are different
assert np.abs(noise1 - noise2).sum() != 0
# Save the first waveform in a file.
with NamedTemporaryFile(suffix=".wav") as f:
torchaudio.save(f.name, torch.from_numpy(noise1), sample_rate=16000)
recording = Recording.from_file(f.name)
# Read the audio -- should be equal to noise1.
audio = recording.load_audio()
np.testing.assert_almost_equal(audio, noise1)
# Save noise2 to the same location.
torchaudio.save(f.name, torch.from_numpy(noise2), sample_rate=16000)
# Read the audio -- should be equal to noise2,
# and the caching is ignored (doesn't happen).
audio = recording.load_audio()
np.testing.assert_almost_equal(audio, noise2)
def main():
weights_dir: str = os.path.expanduser("~/git/cherokee-diffwave/models/")
cd_script_dir()
model_pt = os.path.join(weights_dir, "weights.pt")
npy_files: List[str] = list()
npy_files.extend(sorted(glob.glob("?.npy")))
npy_files.extend(sorted(glob.glob("??.npy")))
npy_files.extend(sorted(glob.glob("???.npy")))
npy_files.extend(sorted(glob.glob("????.npy")))
npy_files.extend(sorted(glob.glob("?????.npy")))
bar: ProgressBar = progressbar.ProgressBar(maxval=len(npy_files))
bar.start()
npy_wav_files: List[Tuple[str, str]] = list()
for npy_file in npy_files:
wav_file = f"wg-{os.path.splitext(npy_file)[0]}.wav"
npy_wav_files.append((npy_file, wav_file))
if os.path.isfile(wav_file):
os.remove(wav_file)
for npy_file, wav_file in npy_wav_files:
nd_array = numpy.load(npy_file)
spectrogram: Tensor = torch.from_numpy(nd_array).float()
spectrogram = torch.clamp((spectrogram + 100) / 100, 0.0, 1.0)
audio, sr = diffwave_predict(spectrogram,
model_pt,
device=torch.device("cuda"))
torchaudio.save(wav_file, audio.cpu(), sample_rate=sr)
bar.update(bar.currval + 1)
bar.finish()
def _save_audio(self, audios, fs, feature_names, save2):
for feature_name in feature_names:
audio = audios[feature_name]
audio = audio.squeeze(0) if len(audio.shape) > 2 else audio
torchaudio.save(
pjoin(save2, feature_name) + '.wav', audio,
fs['audio_sr'].item())
def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print('Using device: {}'.format(device))
data, sample_rates = load(enforce_samplerate=44100)
print(data)
# 10 seconds of audio
seq_len = 60
print('Loading trained model...')
model = load_model_from_checkpoint(TRAINED_STATE, device)
print('Performing inference...')
prev, _ = get_batch(data, 10, 1, device, segment_size=44100, full=True)
print('Encoding seed sequence')
hidden = model.encode(prev)
print('Producing sequence')
audio = torch.clamp(model.decode(hidden, seq_len), -1, 1)
print('Saving result')
np.save(os.path.join(OUTPUT_DIR, 'prediction.npy'),
audio[0].detach().cpu().numpy())
if not IS_WINDOWS:
import torchaudio
torchaudio.save("prediction.mp3", torch.stack((audio[0], audio[0])),
sample_rates[0])
plt.plot(audio[0].detach().cpu().numpy())
plt.show()
return audio
def segment_audio(
audio_path: str,
channel: int,
start: int,
end: int,
save_path: str,
sample_rate: int = 16000,
device: str = "cpu",
):
"""segment and resample audio"""
start = int(start / 100 * 8000)
end = int(end / 100 * 8000)
num_frames = end - start
data, _ = torchaudio.load(audio_path,
frame_offset=start,
num_frames=num_frames)
resampler = Resample(orig_freq=8000,
new_freq=sample_rate).to(device=device)
data = resampler(data)
data = torch.unsqueeze(data[channel], 0)
torchaudio.save(save_path, src=data, sample_rate=sample_rate)
def write_wavs(self, batch_id, wavs, score, lens):
"""Write wavs to files, for historical discriminator training
Arguments
---------
batch_id : list of str
A list of the utterance ids for the batch
wavs : torch.Tensor
The wavs to write to files
score : torch.Tensor
The actual scores for the corresponding utterances
lens : torch.Tensor
The relative lengths of each utterance
"""
lens = lens * wavs.shape[1]
record = {}
for i, (name, pred_wav, length) in enumerate(zip(batch_id, wavs, lens)):
path = os.path.join(self.hparams.MetricGAN_folder, name + ".wav")
data = torch.unsqueeze(pred_wav[: int(length)].cpu(), 0)
torchaudio.save(path, data, self.hparams.Sample_rate)
# Make record of path and score for historical training
score = float(score[i][0])
record[name] = {
"enh_wav": path,
"score": score,
}
# Update records for historical training
self.historical_set.update(record)
with open(self.hparams.historical_file, "wb") as fp: # Pickling
pickle.dump(self.historical_set, fp)
def setUpClass(cls):
if not os.path.exists(cls._AUDIO_DATA_DIR):
os.makedirs(cls._AUDIO_DATA_DIR)
if not os.path.exists(cls._AUDIO_LIST_DIR):
os.makedirs(cls._AUDIO_LIST_DIR)
with open(cls._JUNK_FILE, "w") as f:
f.write("this is some garbage\nShould have no impact.")
with open(cls._AUDIO_LIST_PATHS_PATH, "w") as f_list_fnames, \
open(cls._AUDIO_LIST_FNAMES_PATH, "w") as f_list_paths:
lengths = torch.randint(int(.5e5), int(1.5e6), (cls._N_EXAMPLES,))
for i in range(cls._N_EXAMPLES):
# dividing gets the noise in [-1, 1]
white_noise = torch.randn((cls._N_CHANNELS, lengths[i])) / 10
f_path = cls._AUDIO_DATA_PATH_FMT.format(i)
torchaudio.save(f_path, white_noise, cls._SAMPLE_RATE)
f_name_short = cls._AUDIO_DATA_FMT.format(i)
f_list_fnames.write(f_name_short + "\n")
f_list_paths.write(f_path + "\n")
import argparse
import torch
import torchaudio
from data.data_loader import load_audio, NoiseInjection
parser = argparse.ArgumentParser()
parser.add_argument('--input-path', default='input.wav', help='The input audio to inject noise into')
parser.add_argument('--noise-path', default='noise.wav', help='The noise file to mix in')
parser.add_argument('--output-path', default='output.wav', help='The noise file to mix in')
parser.add_argument('--sample-rate', default=16000, help='Sample rate to save output as')
parser.add_argument('--noise-level', type=float, default=1.0,
help='The Signal to Noise ratio (higher means more noise)')
args = parser.parse_args()
noise_injector = NoiseInjection()
data = load_audio(args.input_path)
mixed_data = noise_injector.inject_noise_sample(data, args.noise_path, args.noise_level)
mixed_data = torch.tensor(mixed_data, dtype=torch.float).unsqueeze(1) # Add channels dim
torchaudio.save(args.output_path, mixed_data, args.sample_rate)
print('Saved mixed file to %s' % args.output_path)