def DeepTalk_encoder(file_path,
model_save_path,
module_name,
preprocess=True,
normalize=True,
sampling_rate=8000,
duration=None):
encoder.load_model(model_save_path, module_name=module_name)
if (preprocess):
wav = Synthesizer.load_preprocess_wav(file_path)
ref_audio = encoder.preprocess_wav(wav)
else:
ref_audio, sr = librosa.load(file_path, sr=sampling_rate)
if (duration is not None):
ref_audio = ref_audio[0:int(duration * sampling_rate)]
embed, partial_embeds, _ = encoder.embed_utterance(ref_audio,
using_partials=True,
return_partials=True)
if (normalize):
embed = embed / np.linalg.norm(embed)
return embed
def synth(text, audio_file):
"""
Parameters
----------
text : string
text to be said in synthesized voice
audio_file : filepath
filepath for audio file in wav format
Returns
-------
generated_wav : numpy.ndarray
Numpy padded array of synthesized audio signal
"""
in_fpath = Path("audio.wav")
original_wav, sampling_rate = librosa.load(in_fpath)
preprocessed_wav = encoder.preprocess_wav(original_wav, sampling_rate)
embed = encoder.embed_utterance(preprocessed_wav)
print("Synthesizing new audio...")
specs = synthesizer.synthesize_spectrograms([text], [embed])
generated_wav = vocoder.infer_waveform(specs[0])
generated_wav = np.pad(generated_wav, (0, synthesizer.sample_rate),
mode="constant")
return generated_wav
def change_mode(character: str = "Human_Man", tone: str = "neutral"):
training_dir = voices_dict[character]['ID']
tone_file = voices_dict[character]['tone'][tone] + '.flac'
tone_dir = tone_file.split("-")[1]
local_infpath = Path(f'{data_path}/{training_dir}/{tone_dir}/{tone_file}')
global in_fpath, filenum, preprocessed_wav, embed, torch, vocoder
if local_infpath != in_fpath and character is not None:
if tone is None:
tone = "neutral"
print(
f'Reference sound has changed; now loading {character}:{tone}...')
with nostdout():
in_fpath = local_infpath
preprocessed_wav = encoder.preprocess_wav(in_fpath)
original_wav, sampling_rate = librosa.load(str(in_fpath))
preprocessed_wav = encoder.preprocess_wav(original_wav,
sampling_rate)
embed = encoder.embed_utterance(preprocessed_wav)
torch.manual_seed(seed)
vocoder.load_model(vocoder_path)
text_to_speech('Tea.', play_sound=False)
else:
print('Mode is already correct. No need to change.')
def __init__(self):
if (Text2SpeechModel == "dc_tts"):
self.g = Graph(mode="synthesize")
print("Text2Speech Tensorflow Graph loaded")
elif (Text2SpeechModel == "RTVC"):
enc_model_fpath = os.path.join(
root_file_path, "RTVC", "encoder/saved_models/pretrained.pt")
syn_model_dir = os.path.join(
root_file_path, "RTVC",
"synthesizer/saved_models/logs-pretrained")
voc_model_fpath = os.path.join(
root_file_path, "RTVC",
"vocoder/saved_models/pretrained/pretrained.pt")
encoder.load_model(enc_model_fpath)
self.synthesizer = Synthesizer(os.path.join(
syn_model_dir, "taco_pretrained"),
low_mem=False)
vocoder.load_model(voc_model_fpath)
in_fpath = os.path.join("/",
*root_file_path.split("/")[:-1],
"REF/refaudioRTVC/ref.wav")
preprocessed_wav = encoder.preprocess_wav(in_fpath)
original_wav, sampling_rate = librosa.load(in_fpath)
preprocessed_wav = encoder.preprocess_wav(original_wav,
sampling_rate)
embed = encoder.embed_utterance(preprocessed_wav)
self.embeds = [embed]
elif (Text2SpeechModel == "AudioSynth"):
taco_pretrained_config_path = os.path.join(
root_file_path,
'AudioSynth/TensorFlowTTS/examples/tacotron2/conf/tacotron2.v1.yaml'
)
tacotron2_config = AutoConfig.from_pretrained(
taco_pretrained_config_path)
taco_path = os.path.join(root_file_path,
"AudioSynth/tacotron2-120k.h5")
self.tacotron2 = TFAutoModel.from_pretrained(
config=tacotron2_config,
pretrained_path=taco_path,
training=False,
name="tacotron2")
melgan_stft_pretrained_config_path = os.path.join(
root_file_path,
'AudioSynth/TensorFlowTTS/examples/melgan.stft/conf/melgan.stft.v1.yaml'
)
melgan_stft_config = AutoConfig.from_pretrained(
melgan_stft_pretrained_config_path)
melgan_stft_path = os.path.join(root_file_path,
"AudioSynth/melgan.stft-2M.h5")
self.melgan_stft = TFAutoModel.from_pretrained(
config=melgan_stft_config,
pretrained_path=melgan_stft_path,
name="melgan_stft")
self.processor = AutoProcessor.from_pretrained(
pretrained_path=os.path.join(
root_file_path, "AudioSynth/ljspeech_mapper.json"))
mels, alignment_history, audios = do_synthesis(
"Hello, how can I help you today?", self.tacotron2,
self.melgan_stft, "TACOTRON", "MELGAN-STFT", self.processor)
def preprocess_embeddings(self, path, ext_audio, ext_embed):
for i in range(1, len(self._walker)):
fileid = self._walker[i]
speaker_id, chapter_id, utterance_id = fileid.split("-")
fileid_audio = speaker_id + "-" + chapter_id + "-" + utterance_id
file_audio = fileid_audio + ext_audio
file_audio = os.path.join(path, speaker_id, chapter_id, file_audio)
file_embed = fileid_audio + ext_embed
file_embed = os.path.join(path, speaker_id, chapter_id, file_embed)
# Load audio
waveform, sample_rate = torchaudio.load(file_audio)
print("Loaded file: ", fileid)
# Calculate speaker embedding
wav = waveform.transpose(0, 1).detach().numpy().squeeze()
preprocessed_wav = styleEncoder.preprocess_wav(wav, sample_rate)
embedding = styleEncoder.embed_utterance(preprocessed_wav)
# Save embeddings to corresponding csv files
data = asarray(embedding)
savetxt(file_embed, data, delimiter=',')
print("Saved embedding: ", file_embed)
def generate_voice(self, in_fpath, text, out_fpath):
try:
preprocessed_wav = encoder.preprocess_wav(in_fpath)
original_wav, sampling_rate = librosa.load(in_fpath)
preprocessed_wav = encoder.preprocess_wav(original_wav,
sampling_rate)
print("Loaded file successfully")
embed = encoder.embed_utterance(preprocessed_wav)
print("Created the embedding")
texts = [text]
embeds = [embed]
# If you know what the attention layer alignments are, you can retrieve them here by
# passing return_alignments=True
specs = self.synthesizer.synthesize_spectrograms(texts, embeds)
spec = specs[0]
print("Created the mel spectrogram")
## Generating the waveform
print("Synthesizing the waveform:")
generated_wav = vocoder.infer_waveform(spec)
generated_wav = np.pad(generated_wav,
(0, self.synthesizer.sample_rate),
mode="constant")
librosa.output.write_wav(out_fpath,
generated_wav.astype(np.float32),
self.synthesizer.sample_rate)
print("\nSaved output as %s\n\n" % out_fpath)
except Exception as e:
print("Caught exception: %s" % repr(e))
print("Restarting\n")
def vocode(self):
speaker_name, spec, breaks, _ = self.current_generated
assert spec is not None
# Synthesize the waveform
if not vocoder.is_loaded():
self.init_vocoder()
def vocoder_progress(i, seq_len, b_size, gen_rate):
real_time_factor = (gen_rate / Synthesizer.sample_rate) * 1000
line = "Waveform generation: %d/%d (batch size: %d, rate: %.1fkHz - %.2fx real time)" \
% (i * b_size, seq_len * b_size, b_size, gen_rate, real_time_factor)
self.ui.log(line, "overwrite")
self.ui.set_loading(i, seq_len)
if self.ui.current_vocoder_fpath is not None:
self.ui.log("")
wav = vocoder.infer_waveform(spec, progress_callback=vocoder_progress)
else:
self.ui.log("Waveform generation with Griffin-Lim... ")
wav = Synthesizer.griffin_lim(spec)
self.ui.set_loading(0)
self.ui.log(" Done!", "append")
# Add breaks
b_ends = np.cumsum(np.array(breaks) * Synthesizer.hparams.hop_size)
b_starts = np.concatenate(([0], b_ends[:-1]))
wavs = [wav[start:end] for start, end, in zip(b_starts, b_ends)]
breaks = [np.zeros(int(0.15 * Synthesizer.sample_rate))] * len(breaks)
wav = np.concatenate([i for w, b in zip(wavs, breaks) for i in (w, b)])
# Play it
wav = wav / np.abs(wav).max() * 0.97
self.ui.play(wav, Synthesizer.sample_rate)
fref = '-'.join([self.ui.current_dataset_name, self.ui.current_speaker_name, self.ui.current_utterance_name])
ftime = '{}'.format(int(time.time()))
ftext = self.ui.text_prompt.toPlainText()
fms = int(len(wav) * 1000 / Synthesizer.sample_rate)
fname = filename_formatter('{}_{}_{}ms_{}.wav'.format(fref, ftime, fms, ftext))
audio.save_wav(wav, _out_wav_dir.joinpath(fname), Synthesizer.sample_rate) # save
# Compute the embedding
# TODO: this is problematic with different sampling rates, gotta fix it
if not encoder.is_loaded():
self.init_encoder()
encoder_wav = encoder.preprocess_wav(wav)
embed, partial_embeds, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
# Add the utterance
name = speaker_name + "_gen_%05d" % int(time.time())
utterance = Utterance(name, speaker_name, wav, spec, embed, partial_embeds, True)
np.save(_out_embed_dir.joinpath(name + '.npy'), embed, allow_pickle=False) # save
self.utterances.add(utterance)
# Plot it
self.ui.draw_embed(embed, name, "generated")
self.ui.draw_umap_projections(self.utterances)
def predict(self, path):
# fpath = '/home/ali/Desktop/a2lsv/deneme/'
fpaths = glob(path+"/*.wav")
embedings = []
embedingsDict = {}
for fpath in fpaths:
wav = librosa.load(fpath, 16000)[0]
encoder_wav = encoder.preprocess_wav(wav)
embed, partial_embeds, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
embed = np.array(embed).reshape(-1)
embedings.append(embed)
embedingsDict[fpath.split("/")[-1].split(".wav")[0]] = embed
pickle.dump(embedingsDict, open(path+"/embedingsDict.pickle", 'wb'))
# reducer = TSNE()
reducer = umap.UMAP(int(np.ceil(np.sqrt(len(embedings)))), metric="cosine")
projections = reducer.fit_transform(embedings)
thresh = 1
clusters = hcluster.fclusterdata(projections, thresh, criterion="distance")
speakerSlices = {}
for fpath, speaker in zip(fpaths, clusters):
speaker = str(speaker)
audioId = fpath.split('/')[-1].split('.')[0]
if speaker not in speakerSlices.keys():
speakerSlices[speaker] = [int(audioId)]
else:
speakerSlices[speaker] += [int(audioId)]
for k, v in speakerSlices.items():
v.sort()
return speakerSlices
def add_real_utterance(self, wav, name, speaker_name):
# Compute the mel spectrogram
spec = Synthesizer.make_spectrogram(wav)
self.ui.draw_spec(spec, "current")
# Compute the embedding
if not encoder.is_loaded():
self.init_encoder()
encoder_wav = encoder.preprocess_wav(wav)
embed, partial_embeds, _ = encoder.embed_utterance(
encoder_wav, return_partials=True)
np.save(self._out_embed_dir.joinpath(name + '.npy'),
embed,
allow_pickle=False) # save
# Add the utterance
utterance = Utterance(name, speaker_name, wav, spec, embed,
partial_embeds, False)
self.utterances.add(utterance)
self.ui.register_utterance(utterance)
# Plot it
self.ui.draw_embed(embed, name, "current")
self.ui.draw_umap_projections(self.utterances)
def vocode(self):
speaker_name, spec, breaks, _ = self.current_generated
assert spec is not None
# Synthesize the waveform
if not vocoder.is_loaded():
self.init_vocoder()
def vocoder_progress(i, seq_len, b_size, gen_rate):
real_time_factor = (gen_rate / Synthesizer.sample_rate) * 1000
line = "Waveform generation: %d/%d (batch size: %d, rate: %.1fkHz - %.2fx real time)" \
% (i * b_size, seq_len * b_size, b_size, gen_rate, real_time_factor)
self.ui.log(line, "overwrite")
self.ui.set_loading(i, seq_len)
if self.ui.current_vocoder_fpath is not None:
self.ui.log("")
wav = vocoder.infer_waveform(spec,
progress_callback=vocoder_progress)
else:
self.ui.log("Waveform generation with Griffin-Lim... ")
wav = Synthesizer.griffin_lim(spec)
self.ui.set_loading(0)
self.ui.log(" Done!", "append")
# Add breaks
b_ends = np.cumsum(np.array(breaks) * Synthesizer.hparams.hop_size)
b_starts = np.concatenate(([0], b_ends[:-1]))
wavs = [wav[start:end] for start, end, in zip(b_starts, b_ends)]
breaks = [np.zeros(int(0.15 * Synthesizer.sample_rate))] * len(breaks)
wav = np.concatenate([i for w, b in zip(wavs, breaks) for i in (w, b)])
# Play it
wav = wav / np.abs(wav).max() * 0.97
self.ui.play(wav, Synthesizer.sample_rate)
self.ui.save_button.setDisabled(False)
# Compute the embedding
# TODO: this is problematic with different sampling rates, gotta fix it
if not encoder.is_loaded():
self.init_encoder()
encoder_wav = encoder.preprocess_wav(wav)
embed, partial_embeds, _ = encoder.embed_utterance(
encoder_wav, return_partials=True)
# Add the utterance
if not speaker_name is None:
name = speaker_name
else:
name = "unknown"
name = name + "_gen_%05d" % np.random.randint(100000)
utterance = Utterance(name, speaker_name, wav, spec, embed,
partial_embeds, True)
self.utterances.add(utterance)
self.ui.register_utterance(utterance)
# Plot it
self.ui.draw_embed(embed, name, "generated")
self.ui.draw_umap_projections(self.utterances)
def load_model(in_fpath, parser):
parser.add_argument("-e", "--enc_model_fpath", type=Path,
default="encoder/saved_models/pretrained.pt",
help="Path to a saved encoder")
parser.add_argument("-s", "--syn_model_dir", type=Path,
default="synthesizer/saved_models/logs-pretrained/",
help="Directory containing the synthesizer model")
parser.add_argument("-v", "--voc_model_fpath", type=Path,
default="vocoder/saved_models/pretrained/pretrained.pt",
help="Path to a saved vocoder")
parser.add_argument("--low_mem", action="store_true", help=\
"If True, the memory used by the synthesizer will be freed after each use. Adds large "
"overhead but allows to save some GPU memory for lower-end GPUs.")
parser.add_argument("--no_sound", action="store_true", help=\
"If True, audio won't be played.")
args = parser.parse_args()
encoder.load_model(args.enc_model_fpath)
synthesizer = Synthesizer(args.syn_model_dir.joinpath("taco_pretrained"), low_mem=args.low_mem)
vocoder.load_model(args.voc_model_fpath)
preprocessed_wav = encoder.preprocess_wav(in_fpath)
original_wav, sampling_rate = librosa.load(in_fpath)
preprocessed_wav = encoder.preprocess_wav(original_wav, sampling_rate)
embed = encoder.embed_utterance(preprocessed_wav)
return synthesizer, sampling_rate, embed
def initialize(self):
print("Running a test of your configuration...\n")
if not torch.cuda.is_available():
print(
"Your PyTorch installation is not configured to use CUDA. If you have a GPU ready "
"for deep learning, ensure that the drivers are properly installed, and that your "
"CUDA version matches your PyTorch installation. CPU-only inference is currently "
"not supported.")
quit(-1)
print("PyTorch is available and working...")
device_id = torch.cuda.current_device()
gpu_properties = torch.cuda.get_device_properties(device_id)
print(
"Found %d GPUs available. Using GPU %d (%s) of compute capability %d.%d with "
"%.1fGb total memory.\n" %
(torch.cuda.device_count(), device_id, gpu_properties.name,
gpu_properties.major, gpu_properties.minor,
gpu_properties.total_memory / 1e9))
## Load the models one by one.
print("Preparing the encoder, the synthesizer and the vocoder...")
encoder.load_model(self.enc_model_fpath)
vocoder.load_model(self.voc_model_fpath)
## Run a test
print("Testing your configuration with small inputs.")
print("\tTesting the encoder...")
encoder.embed_utterance(np.zeros(encoder.sampling_rate))
embed = np.random.rand(speaker_embedding_size)
embed /= np.linalg.norm(embed)
embeds = [embed, np.zeros(speaker_embedding_size)]
texts = ["test 1", "test 2"]
print(
"\tTesting the synthesizer... (loading the model will output a lot of text)"
)
mels = self.synthesizer.synthesize_spectrograms(texts, embeds)
mel = np.concatenate(mels, axis=1)
no_action = lambda *args: None
print("\tTesting the vocoder...")
vocoder.infer_waveform(mel,
target=200,
overlap=50,
progress_callback=no_action)
print("All test passed! You can now synthesize speech.\n\n")
def transform_embed(wav, encoder_model_fpath=Path()):
from encoder import inference as encoder
if not encoder.is_loaded():
encoder.load_model(encoder_model_fpath)
wav = encoder.preprocess_wav(wav)
embed = encoder.embed_utterance(wav)
return embed
def embed_utterance(fpaths, encoder_model_fpath):
if not encoder.is_loaded():
encoder.load_model(encoder_model_fpath)
# Compute the speaker embedding of the utterance
wav_fpath = fpaths
wav, rate = librosa.load(wav_fpath)
wav = encoder.preprocess_wav(wav, rate)
return encoder.embed_utterance(wav)
def extract_utterance_feats_spkr(self, data_utterance_path, is_full_ppg=False):
"""Get PPG and Mel (+ optional F0) for an utterance.
Args:
data_utterance_path: The path to the data utterance protocol buffer.
is_full_ppg: If True, will use the full PPGs.
Returns:
feat_pairs: A list, each is a [pps, mel, dvec(spkr embedding)] pair.
"""
utt = Utterance()
fs, wav = wavfile.read(data_utterance_path)
utt.fs = fs
utt.wav = wav
utt.ppg = get_ppg(data_utterance_path, self.ppg_deps)
audio = torch.FloatTensor(utt.wav.astype(np.float32))
fs = utt.fs
if fs != self.stft.sampling_rate:
raise ValueError("{} SR doesn't match target {} SR".format(
fs, self.stft.sampling_rate))
audio_norm = audio / self.max_wav_value
audio_norm = audio_norm.unsqueeze(0)
audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
# (1, n_mel_channels, T)
acoustic_feats = self.stft.mel_spectrogram(audio_norm)
# (n_mel_channels, T)
acoustic_feats = torch.squeeze(acoustic_feats, 0)
# (T, n_mel_channels)
acoustic_feats = acoustic_feats.transpose(0, 1)
#print("encoder model path", self.encoder_model_fpath)
from encoder import inference as encoder
if not encoder.is_loaded():
encoder.load_model(self.encoder_model_fpath)
#wav = np.load(data_utterance_path)
wav = encoder.preprocess_wav(data_utterance_path) # wav
embed = encoder.embed_utterance(wav)
#print("spkr embedding", embed)
#print("shape of ppg, acoustic feats and spkr embedding", (utt.ppg).shape, acoustic_feats.shape, embed.shape)
if is_full_ppg:
if self.is_append_f0:
ppg_f0 = append_ppg(utt.ppg, utt.f0)
return [ppg_f0, acoustic_feats, embed]
else:
return [utt.ppg, acoustic_feats, embed]
else:
if self.is_append_f0:
ppg_f0 = append_ppg(utt.monophone_ppg, utt.f0)
return [ppg_f0, acoustic_feats, embed]
else:
return [utt.monophone_ppg, acoustic_feats, embed]
def embed_utterance(fpaths, encoder_model_fpath):
if not encoder.is_loaded():
encoder.load_model(encoder_model_fpath)
# Compute the speaker embedding of the utterance
wav_fpath, embed_fpath = fpaths
wav = np.load(wav_fpath)
wav = encoder.preprocess_wav(wav)
embed = encoder.embed_utterance(wav)
np.save(embed_fpath, embed, allow_pickle=False)
def get_embed(self, wav):
# from encoder import inference as encoder
if not encoder.is_loaded():
encoder.load_model(self.encoder_model_fpath, device='cpu')
# 用cpu避免以下报错。
# "RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the ‘spawn’ start method"
wav = encoder.preprocess_wav(wav)
embed = encoder.embed_utterance(wav)
return embed
def get_spk_embed(load_path, enc_model_fpath):
file_name = load_path.split('/')[-1]
wav = load_wav(load_path)
encoder.load_model(enc_model_fpath)
preprocessed_wav = encoder.preprocess_wav(load_path)
embed = encoder.embed_utterance(preprocessed_wav)
spk_embd = torch.tensor(embed).unsqueeze(0)
return spk_embd, file_name
def embed_utterance(fpaths, encoder_model_fpath):
if not encoder.is_loaded():
encoder.load_model(encoder_model_fpath)
# Compute the speaker embedding of the utterance
wav_fpath = embed_fpath = fpaths
embed_fpath = embed_fpath.replace(".wav", ".npy")
wav, rate = librosa.load(wav_fpath)
wav = encoder.preprocess_wav(wav, rate)
embed = encoder.embed_utterance(wav)
np.save(embed_fpath, embed, allow_pickle=False)
def run_voiceCloning(filename):
in_fpath = dataPath + "/" + filename
#transforming mp3 into wav
subprocess.call(['ffmpeg', '-i', in_fpath + '.mp3', in_fpath + '.wav'])
time.sleep(5)
#running the encoder on the audio input
original_wav, sampling_rate = librosa.load(Path(in_fpath + '.wav'))
preprocessed_wav = encoder.preprocess_wav(original_wav, sampling_rate)
#getting the embeds from the encoder
embed = encoder.embed_utterance(preprocessed_wav)
return audioFromEmbeds(filename, embed)
def embed_utterance(src, skip_existing=True, encoder_model_fpath=Path()):
if not encoder.is_loaded():
encoder.load_model(encoder_model_fpath)
wav_fpath, embed_fpath = src
if skip_existing and embed_fpath.is_file():
return
wav = aukit.load_wav(wav_fpath, sr=hp.sampling_rate)
wav = encoder.preprocess_wav(wav)
embed = encoder.embed_utterance(wav)
np.save(embed_fpath, embed, allow_pickle=False)
def clone_voice(sentence, results_file):
"""Adapted from 'demo_cli.py'"""
u_path = Path('utterance.wav')
results_path = Path(results_file)
preprocessed_wav = encoder.preprocess_wav(u_path)
embed = encoder.embed_utterance(preprocessed_wav)
specs = synthesizer.synthesize_spectrograms([sentence], [embed])
generated_wav = vocoder.infer_waveform(specs[0])
generated_wav = np.pad(generated_wav, (0, synthesizer.sample_rate), mode="constant")
librosa.output.write_wav(results_path, generated_wav.astype(np.float32),
synthesizer.sample_rate)
def embed_utterance(fpaths, encoder_model_fpath):
if not encoder.is_loaded():
encoder.load_model(encoder_model_fpath)
# Compute the speaker embedding of the utterance
wav_fpath, embed_fpath, _ = fpaths
# try:
# wav = np.load(wav_fpath)
# except ValueError as e:
# print(e)
# wav = np.load(wav_fpath, allow_pickle=True)
wav = encoder.preprocess_wav(wav_fpath)
embed = encoder.embed_utterance(wav)
np.save(embed_fpath, embed, allow_pickle=False)
def gen_audio(self, ref_audio, text):
try:
in_fpath = Path(ref_audio.replace("\"", "").replace("\'", ""))
## Computing the embedding
# First, we load the wav using the function that the speaker encoder provides. This is
# important: there is preprocessing that must be applied.
# The following two methods are equivalent:
# - Directly load from the filepath:
# preprocessed_wav = encoder.preprocess_wav(in_fpath)
# - If the wav is already loaded:
original_wav, sampling_rate = librosa.load(in_fpath, sr=None)
preprocessed_wav = encoder.preprocess_wav(original_wav, sampling_rate)
print("Loaded file succesfully, rate=%s" % sampling_rate)
# Then we derive the embedding. There are many functions and parameters that the
# speaker encoder interfaces. These are mostly for in-depth research. You will typically
# only use this function (with its default parameters):
embed = encoder.embed_utterance(preprocessed_wav)
print("Created the embedding")
## Generating the spectrogram
# The synthesizer works in batch, so you need to put your data in a list or numpy array
texts = [text]
embeds = [embed]
# If you know what the attention layer alignments are, you can retrieve them here by
# passing return_alignments=True
specs = self.synthesizer.synthesize_spectrograms(texts, embeds)
spec = specs[0]
print("Created the mel spectrogram")
## Generating the waveform
print("Synthesizing the waveform:")
# Synthesizing the waveform is fairly straightforward. Remember that the longer the
# spectrogram, the more time-efficient the vocoder.
generated_wav = vocoder.infer_waveform(spec)
## Post-generation
# There's a bug with sounddevice that makes the audio cut one second earlier, so we
# pad it.
# generated_wav = np.pad(generated_wav, (0, self.synthesizer.sample_rate), mode="constant")
print("\n samples = %s @ %s" % (len(generated_wav), self.synthesizer.sample_rate))
return generated_wav
except Exception as e:
traceback.print_exc()
print("Caught exception: %s" % repr(e))
print("Restarting\n")
def create_embedding(path):
if EMBEDDING_TYPE == "ge2e":
in_fpath = Path(path.replace("\"", "").replace("\'", ""))
preprocessed_wav = encoder.preprocess_wav(in_fpath)
return encoder.embed_utterance(preprocessed_wav)
else:
# in_fpath = Path(path.replace("\"", "").replace("\'", ""))
# preprocessed_wav = encoder.preprocess_wav(in_fpath)
samplerate, data = read(path.replace("\"", "").replace("\'", ""))
if samplerate != 16000:
sys.exit(f"{path} does not have sample rate of 16000")
emb = module(samples=np.array(data, dtype=float),
sample_rate=16000)['embedding']
emb.shape.assert_is_compatible_with([None, 2048])
return emb.numpy()
def signup(wav_fpath: Path, username, encoder_model_fpath):
if not encoder.is_loaded():
encoder.load_model(encoder_model_fpath)
# Compute the speaker embedding of the utterance
embed_fpath = signup_dir.joinpath(username + ".npy")
wav = encoder.preprocess_wav(str(wav_fpath))
embed = encoder.embed_utterance(wav)
if os.path.exists(embed_fpath):
old_embed = np.load(embed_fpath)
embed = old_embed + embed
embed /= np.linalg.norm(embed, 2)
os.remove(embed_fpath)
np.save(embed_fpath, embed, allow_pickle=False)
print(username + " signed up.")
def transform_embed(wav, encoder_model_fpath=Path()):
# from encoder import inference as encoder
if not encoder.is_loaded():
encoder.load_model(encoder_model_fpath)
wav_ = encoder.preprocess_wav(wav)
# Take segment
segment_length = 2 * encoder.sampling_rate # 随机选取2秒语音生成语音表示向量
if len(wav_) > segment_length:
max_audio_start = len(wav_) - segment_length
audio_start = random.randint(0, max_audio_start)
wav_ = wav_[audio_start:audio_start + segment_length]
embed = encoder.embed_utterance(wav_)
return embed
def _compute_embedding(audio):
'''
Description
Loading Embedding from the audio file to clone
Input:
audio: Audio File
Output
Embeddings
'''
global embedding
embedding = None
embedding = encoder.embed_utterance(encoder.preprocess_wav(audio, SAMPLE_RATE))
def tts(input_dict):
'''
Flow:
0) Check if audio has embeddings (Not yet)
1) Encode the audio
2) Synthesizer the text with embeddings
3) Vocoder out the fake wav
'''
# init
output_dict = {"data": {}}
# loop the input
for audio_name, raw_audio in input_dict["data"].items():
wav_name_no_ext = Path(audio_name).stem
saved_path_obj = Path.cwd() / "data/output"
# step 1
print("Step 1")
raw_audio_np, sample_rate = librosa.load(io.BytesIO(raw_audio))
preprocessed_wav = encoder.preprocess_wav(raw_audio_np, sample_rate)
embeddings = encoder.embed_utterance(preprocessed_wav)
# step 2
print("Step 2")
splitted_text = input_dict["text"].split(".")
clean_text_list = [text for text in splitted_text if len(text) > 0]
if len(clean_text_list) == 0:
raise Exception("Empty text field")
sentence_count = len(clean_text_list)
embeddings_list = [embeddings] * sentence_count
specs = synthesizer.synthesize_spectrograms(clean_text_list,
embeddings_list)
# step 3
print("Step 3")
for index, spec in enumerate(specs):
generated_wav = vocoder.infer_waveform(spec)
# needed to 1 second for playback capability
generated_wav = np.pad(generated_wav, (0, synthesizer.sample_rate),
mode="constant")
file_name = "{}_tts_{}.wav".format(wav_name_no_ext, index)
file_path = saved_path_obj / file_name
sf.write(str(file_path), generated_wav.astype(np.float32),
synthesizer.sample_rate, 'PCM_16')
output_dict["data"][index] = file_path
return output_dict
def embed_utterance(fpaths, encoder_model_fpath, hparams):
if not encoder.is_loaded():
encoder.load_model(encoder_model_fpath)
# Compute the speaker embedding of the utterance
wav_fpath, embed_fpath = fpaths
if embed_fpath.exists():
return
# wav = np.load(wav_fpath)
wav, _ = librosa.load(wav_fpath, hparams.sample_rate)
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
wav = encoder.preprocess_wav(wav)
embed = encoder.embed_utterance(wav)
np.save(embed_fpath, embed, allow_pickle=False)
