more tweaks
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804ddb5182
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8f41d1b324
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@ -40,6 +40,8 @@ def load_audio( path ):
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return waveform, sr
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tts = None
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def process(
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speaker_path,
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yaml,
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@ -52,7 +54,12 @@ def process(
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verbose=False,
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metadata_path=None,
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maximum_duration=0,
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#use_faiss=True,
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):
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global tts
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cfg.set_audio_backend(audio_backend)
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artifact_extension = cfg.audio_backend_extension
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@ -60,7 +67,8 @@ def process(
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cfg.inference.amp = amp # False
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# easy way to load the model and handle encoding audio
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tts = init_tts( yaml=yaml, restart=False, device=device, dtype=dtype )
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if tts is None:
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tts = init_tts( yaml=yaml, restart=False, device=device, dtype=dtype )
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queue = []
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features = {}
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@ -69,6 +77,13 @@ def process(
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mfcc = T.MFCC(sample_rate=cfg.sample_rate)
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"""
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# too slow
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if use_faiss:
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import faiss
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index = None
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"""
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# compute features (embeddings if quantized already, MFCC features if raw audio)
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for filename in tqdm(os.listdir(f'./{speaker_path}/'), desc=f"Encoding '{speaker_path}'", disable=not verbose):
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extension = filename.split(".")[-1]
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@ -91,20 +106,40 @@ def process(
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if lang != "en":
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phn = phn.replace(f"({metadata['language']})", "")
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features[filename] = tts.text_embedding( phn )
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embedding = tts.text_embedding( phn )
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else:
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# treat embeddings as features, if provided quantized audio
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if extension in artifact_extension:
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artifact = np.load(f'./{speaker_path}/{filename}.{extension}', allow_pickle=True)[()]
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qnt = torch.from_numpy(artifact["codes"].astype(int))[0].t().to(dtype=torch.int16, device=device)
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qnt = trim( qnt, int( cfg.dataset.frames_per_second * 3 ) )
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if maximum_duration > 0:
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qnt = trim( qnt, int( cfg.dataset.frames_per_second * maximum_duration ) )
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features[filename] = tts.audio_embedding( qnt )
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embedding = tts.audio_embedding( qnt )
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# try and extract features from the raw audio itself
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else:
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# qnt = tts.encode_audio(f'./{speaker_path}/{filename}', trim_length=3.0).to(device)
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wav, sr = load_audio( f'./{speaker_path}/{filename}.{extension}' )
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features[filename] = mfcc(wav.to(device))[0].t()
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embedding = mfcc(wav.to(device))[0].t()
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features[filename] = embedding
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"""
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if use_faiss:
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if index is None:
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shape = embedding.shape
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index = faiss.IndexFlatL2(shape[1])
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index.add(embedding.cpu())
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if verbose:
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for filename, embedding in features.items():
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D, I = index.search(embedding.cpu(), k=3)
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# print(f'{filename}: {I[1:]}')
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if metadata_path is not None:
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index.save(metadata_path)
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"""
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keys = list(features.keys())
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key_range = range(len(keys))
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@ -126,6 +161,10 @@ def process(
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similarities[key] = similarity
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# combinations() doesn't have swapped keys
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if swapped_key not in similarities:
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similarities[swapped_key] = similarity
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if index_a not in sorted_similarities:
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sorted_similarities[index_a] = {}
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if index_b not in sorted_similarities[index_a]:
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@ -176,11 +215,12 @@ def main():
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parser.add_argument("--yaml", type=Path)
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parser.add_argument("--text", action="store_true")
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parser.add_argument("--maximum-duration", type=float, default=3.0)
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parser.add_argument("--audio-backend", type=str, default="encodec")
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parser.add_argument("--dtype", type=str, default="bfloat16")
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parser.add_argument("--dtype", type=str, default="float16")
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parser.add_argument("--amp", action="store_true")
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parser.add_argument("--device", type=str, default="cuda")
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parser.add_argument("--device", type=str, default="cpu") # unironically faster
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args = parser.parse_args()
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@ -199,6 +239,7 @@ def main():
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metadata_path=cfg.metadata_dir / f'{speaker_name}.json',
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yaml=args.yaml,
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text=args.text,
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maximum_duration=args.maximum_duration,
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audio_backend=args.audio_backend,
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device=args.device,
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@ -224,6 +265,7 @@ def main():
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speaker_path=args.input_speaker,
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yaml=args.yaml,
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text=args.text,
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maximum_duration=args.maximum_duration,
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audio_backend=args.audio_backend,
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device=args.device,
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