forked from mrq/ai-voice-cloning
remove redundant phonemize for vall-e (oops), quantize all files and then phonemize all files for cope optimization, load alignment model once instead of for every transcription (speedup with whisperx)
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19c0854e6a
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d2a9ab9e41
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@ -1,7 +1,3 @@
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data_root: ./training/${voice}/
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ckpt_root: ./training/${voice}/ckpt/
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log_root: ./training/${voice}/logs/
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data_dirs: [./training/${voice}/valle/]
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spkr_name_getter: "lambda p: p.parts[-3]" # "lambda p: p.parts[-1].split('-')[0]"
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79
src/utils.py
79
src/utils.py
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@ -65,8 +65,7 @@ MAX_TRAINING_DURATION = 11.6097505669
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VALLE_ENABLED = False
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try:
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from vall_e.emb.qnt import encode as quantize
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# from vall_e.emb.g2p import encode as phonemize
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from vall_e.emb.qnt import encode as valle_quantize
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VALLE_ENABLED = True
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except Exception as e:
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@ -80,9 +79,12 @@ tts = None
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tts_loading = False
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webui = None
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voicefixer = None
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whisper_model = None
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whisper_vad = None
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whisper_diarize = None
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whisper_align_model = None
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training_state = None
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current_voice = None
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@ -1165,6 +1167,8 @@ def whisper_transcribe( file, language=None ):
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global whisper_model
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global whisper_vad
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global whisper_diarize
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global whisper_align_model
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if not whisper_model:
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load_whisper_model(language=language)
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@ -1208,7 +1212,7 @@ def whisper_transcribe( file, language=None ):
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else:
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result = whisper_model.transcribe(file)
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align_model, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
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align_model, metadata = whisper_align_model
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result_aligned = whisperx.align(result["segments"], align_model, metadata, file, device)
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if whisper_diarize:
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@ -1462,9 +1466,6 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
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lines = { 'training': [], 'validation': [] }
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segments = {}
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if args.tts_backend == "vall-e":
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phonemize = True
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for filename in results:
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use_segment = use_segments
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@ -1541,6 +1542,11 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
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'phonemes': segment['phonemes'] if 'phonemes' in segment else None
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}
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jobs = {
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'quantize': [[], []],
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'phonemize': [[], []],
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}
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for file in enumerate_progress(segments, desc="Parsing segments", progress=progress):
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result = segments[file]
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path = f'{indir}/audio/{file}'
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@ -1559,8 +1565,6 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
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phonemes = result['phonemes']
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if phonemize and phonemes is None:
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phonemes = phonemizer( text, language=lang )
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if phonemize:
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text = phonemes
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normalized = normalizer(text) if normalize else text
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@ -1587,7 +1591,7 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
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if not culled and audio_length > 0:
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culled = duration < audio_length
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line = f'audio/{file}|{text}'
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line = f'audio/{file}|{phonemes if phonemize and phonemes else text}'
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lines['training' if not culled else 'validation'].append(line)
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@ -1596,16 +1600,42 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
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os.makedirs(f'{indir}/valle/', exist_ok=True)
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if not os.path.exists(f'{indir}/valle/{file.replace(".wav",".qnt.pt")}'):
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from vall_e.emb.qnt import encode as quantize
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quantized = quantize( waveform, sample_rate ).cpu()
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qnt_file = f'{indir}/valle/{file.replace(".wav",".qnt.pt")}'
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if not os.path.exists(qnt_file):
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jobs['quantize'][0].append(qnt_file)
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jobs['quantize'][1].append((waveform, sample_rate))
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"""
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quantized = valle_quantize( waveform, sample_rate ).cpu()
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torch.save(quantized, f'{indir}/valle/{file.replace(".wav",".qnt.pt")}')
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print("Quantized:", file)
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"""
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if not os.path.exists(f'{indir}/valle/{file.replace(".wav",".phn.txt")}'):
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from vall_e.emb.g2p import encode as phonemize
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phonemized = phonemize( normalized )
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phn_file = f'{indir}/valle/{file.replace(".wav",".phn.txt")}'
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if not os.path.exists(phn_file):
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jobs['phonemize'][0].append(phn_file)
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jobs['phonemize'][1].append(normalized)
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"""
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phonemized = valle_phonemize( normalized )
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open(f'{indir}/valle/{file.replace(".wav",".phn.txt")}', 'w', encoding='utf-8').write(" ".join(phonemized))
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print("Phonemized:", file, normalized, text)
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"""
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for i in enumerate_progress(range(len(jobs['quantize'][0])), desc="Quantizing", progress=progress):
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qnt_file = jobs['quantize'][0][i]
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waveform, sample_rate = jobs['quantize'][1][i]
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quantized = valle_quantize( waveform, sample_rate ).cpu()
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torch.save(quantized, qnt_file)
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print("Quantized:", file)
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for i in enumerate_progress(range(len(jobs['phonemize'][0])), desc="Phonemizing", progress=progress):
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phn_file = jobs['phonemize'][0][i]
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normalized = jobs['phonemize'][1][i]
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phonemized = valle_phonemize( normalized )
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open(phn_file, 'w', encoding='utf-8').write(" ".join(phonemized))
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print("Phonemized:", file)
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training_joined = "\n".join(lines['training'])
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validation_joined = "\n".join(lines['validation'])
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@ -2635,6 +2665,7 @@ def load_whisper_model(language=None, model_name=None, progress=None):
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global whisper_model
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global whisper_vad
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global whisper_diarize
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global whisper_align_model
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if args.whisper_backend not in WHISPER_BACKENDS:
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raise Exception(f"unavailable backend: {args.whisper_backend}")
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@ -2683,13 +2714,31 @@ def load_whisper_model(language=None, model_name=None, progress=None):
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device=torch.device(device),
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)
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whisper_diarize = Pipeline.from_pretrained("pyannote/speaker-diarization@2.1",use_auth_token=args.hf_token)
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except Exception as e:
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pass
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whisper_align_model = whisperx.load_align_model(model_name="WAV2VEC2_ASR_LARGE_LV60K_960H" if language=="en" else None, language_code=language, device=device)
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print("Loaded Whisper model")
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def unload_whisper():
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global whisper_model
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global whisper_vad
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global whisper_diarize
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global whisper_align_model
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if whisper_vad:
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del whisper_vad
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whisper_vad = None
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if whisper_diarize:
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del whisper_diarize
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whisper_diarize = None
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if whisper_align_model:
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del whisper_align_model
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whisper_align_model = None
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if whisper_model:
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del whisper_model
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