faster
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be22b65300
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f00283440c
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@ -58,7 +58,7 @@ def process(
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min_duration=0,
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max_duration=0,
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storage_backend="local"
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storage_backend="slop"
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):
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global tts
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@ -72,23 +72,15 @@ def process(
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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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similarities = {}
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sorted_similarities = {}
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mfcc = None
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slop = False # should probably have a better name for this, but it governs whether to just sum the entire sequence of embeddings into one embedding to make life easier
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if storage_backend == "faiss":
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slop = True
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elif storage_backend == "chunkdot":
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slop = True
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elif storage_backend == "slop":
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slop = True
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simplified_metadata = True # aims to slim down the raw data in the JSON to store
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slop = True # should probably have a better name for this, but it governs whether to just sum the entire sequence of embeddings into one embedding to make life easier
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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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for filename in tqdm(os.listdir(f'./{speaker_path}/'), desc=f"Encoding '{speaker_path.name}'", disable=not verbose):
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extension = filename.split(".")[-1]
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filename = filename.replace(f".{extension}", "")
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@ -123,11 +115,13 @@ def process(
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artifact = np.load(f'./{speaker_path}/{filename}.{extension}', allow_pickle=True)[()]
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duration = artifact["metadata"]["original_length"] / artifact["metadata"]["sample_rate"]
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"""
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if 0 < min_duration and duration < min_duration:
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continue
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if 0 < max_duration and max_duration < duration:
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continue
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"""
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qnt = torch.from_numpy(artifact["codes"].astype(int))[0].t().to(dtype=torch.int16, device=device)
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@ -142,11 +136,13 @@ def process(
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duration = wav.shape[-1] / sr
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"""
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if 0 < min_duration and duration < min_duration:
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continue
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if 0 < max_duration and max_duration < duration:
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continue
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"""
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if mfcc is None:
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mfcc = T.MFCC(sample_rate=cfg.sample_rate)
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@ -175,119 +171,20 @@ def process(
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sim = list(I[0][1:])
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print(f'{filename}: {sim}')
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"""
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if metadata_path is not None:
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faiss.write_index(index, str(metadata_path.with_suffix(".faiss")))
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return
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"""
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# to-do: actually refine this, maybe
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# desu it's not super easy to install with python3.12, and it is slower than FAISS in testing............
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if storage_backend == "chunkdot":
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from chunkdot import cosine_similarity_top_k
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embeddings = torch.stack( list( features.values() ) ).cpu().numpy()
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similarities = cosine_similarity_top_k(embeddings, top_k=8, show_progress=verbose)
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print(similarities)
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return
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"""
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metadata = None
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if metadata_path is not None:
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metadata = json.loads(open( metadata_path, "r", encoding="utf-8" ).read()) if metadata_path.exists() else None
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return index
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# do batch cosine similarity processing
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keys = list(features.keys())
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embeddings = torch.stack( list( features.values() ) )
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sorted_similarities = {}
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# do batch cosine similarity processing
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if slop:
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embeddings = torch.stack( list( features.values() ) )
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sorted_similarities = {}
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for index, filename in enumerate(keys):
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embedding = features[filename].unsqueeze(0)
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similarities = torch.nn.functional.cosine_similarity(embedding, embeddings, dim=1).cpu().tolist()
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similarities = sorted([ ( keys[i], similarity ) for i, similarity in enumerate( similarities ) if index != i ], key=lambda x: x[1], reverse=True)
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sorted_similarities[filename] = similarities
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most_filename, most_score = similarities[0]
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least_filename, least_score = similarities[-1]
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if metadata is not None:
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if filename not in metadata:
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metadata[filename] = {}
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metadata[filename]["similar"] = similarities
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if verbose:
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print( f'{filename}:\n\tMost: {most_filename} ({most_score:.3f})\n\tLeast: {least_filename} ({least_score:.3f})' )
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if metadata is not None:
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with open(str(metadata_path), "w", encoding="utf-8") as f:
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f.write( truncate_json( json.dumps( metadata ) ) )
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return sorted_similarities
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# an EXTREMELY naive implementation, fucking disgusting
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queue = list(combinations(range(len(keys)), 2))
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for key in tqdm(queue, desc="Computing similarities", disable=not verbose):
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index_a, index_b = key
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filename_a, filename_b = keys[index_a], keys[index_b]
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swapped_key = (index_b, index_a)
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if swapped_key in similarities:
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similarities[key] = similarities[swapped_key]
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continue
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if slop:
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embedding_a = features[filename_a]
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embedding_b = features[filename_b]
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similarity = torch.nn.functional.cosine_similarity(embedding_a, embedding_b, dim=0).mean().item()
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else:
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shortest = min( features[filename_a].shape[0], features[filename_b].shape[0] )
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embedding_a = features[filename_a][:shortest, :]
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embedding_b = features[filename_b][:shortest, :]
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similarity = torch.nn.functional.cosine_similarity(embedding_a, embedding_b, dim=1).mean().item()
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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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sorted_similarities[index_a][index_b] = similarity
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if index_b not in sorted_similarities:
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sorted_similarities[index_b] = {}
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if index_a not in sorted_similarities[index_b]:
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sorted_similarities[index_b][index_a] = similarity
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# sort similarities scores
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for key, sorted_similarity in sorted_similarities.items():
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sorted_similarities[key] = sorted([ ( key, similarity ) for key, similarity in sorted_similarity.items() ], key=lambda x: x[1], reverse=True)
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most_filename, most_score = sorted_similarities[key][0]
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least_filename, least_score = sorted_similarities[key][-1]
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filename = keys[key]
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if metadata is not None:
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if filename not in metadata:
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metadata[filename] = {}
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metadata[filename]["similar"] = sorted_similarities[key]
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#if verbose:
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# print( f'{filename}:\n\tMost: {most_filename} ({most_score:.3f})\n\tLeast: {least_filename} ({least_score:.3f})' )
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if metadata is not None:
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with open(str(metadata_path), "w", encoding="utf-8") as f:
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f.write( truncate_json( json.dumps( metadata ) ) )
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for filename in tqdm(keys, desc=f"Computing similarities: {speaker_path.name}"):
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embedding = features[filename].unsqueeze(0)
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similarities = torch.nn.functional.cosine_similarity(embedding, embeddings, dim=1).cpu().tolist()
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sorted_similarities[filename] = similarities
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#sorted_similarities[filename] = sorted([ ( i if simplified_metadata else keys[i], similarity ) for i, similarity in enumerate( similarities ) if index != i ], key=lambda x: x[1], reverse=True)
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return sorted_similarities
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@ -299,9 +196,11 @@ 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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"""
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parser.add_argument("--trim-duration", type=float, default=3.0)
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parser.add_argument("--min-duration", type=float, default=0)
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parser.add_argument("--max-duration", type=float, default=0)
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"""
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parser.add_argument("--storage-backend", type=str, default="slop")
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parser.add_argument("--audio-backend", type=str, default="encodec")
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@ -320,15 +219,16 @@ def main():
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speaker_name = name
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if "LibriTTS-R" in speaker_name:
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speaker_name = speaker_name.replace("LibriTTS-R", "LibriVox")
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metadata_path = cfg.metadata_dir / f'{speaker_name}.json'
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process(
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similarities = process(
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speaker_path=cfg.data_dir / speaker_name,
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metadata_path=cfg.metadata_dir / f'{speaker_name}.faiss',
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yaml=args.yaml,
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text=args.text,
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trim_duration=args.trim_duration,
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min_duration=args.min_duration,
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max_duration=args.max_duration,
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#trim_duration=args.trim_duration,
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#min_duration=args.min_duration,
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#max_duration=args.max_duration,
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storage_backend=args.storage_backend,
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audio_backend=args.audio_backend,
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@ -339,6 +239,23 @@ def main():
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verbose=True,
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)
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if args.storage_backend == "faiss":
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faiss.write_index(similarities, str(metadata_path.with_suffix(".faiss")))
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return
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metadata = json.loads(open( metadata_path, "r", encoding="utf-8" ).read()) if metadata_path.exists() else {}
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metadata_keys = list(metadata.keys()) if metadata else list(similarities.keys())
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for filename, sim in similarities.items():
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if filename not in metadata:
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metadata[filename] = {}
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metadata[filename]["similar"] = sim
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with open(str(metadata_path), "w", encoding="utf-8") as f:
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f.write( json.dumps( metadata ) )
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#f.write( truncate_json( json.dumps( metadata ) ) )
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# training
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for data_dir in tqdm(sorted(cfg.dataset.training), desc="Processing Training"):
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add( data_dir, type="training" )
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@ -356,9 +273,10 @@ 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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trim_duration=args.trim_duration,
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min_duration=args.min_duration,
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max_duration=args.max_duration,
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#trim_duration=args.trim_duration,
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#min_duration=args.min_duration,
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#max_duration=args.max_duration,
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audio_backend=args.audio_backend,
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device=args.device,
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