343 lines
9.9 KiB
Python
343 lines
9.9 KiB
Python
"""
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# Handles processing audio provided through --input-audio of adequately annotated transcriptions provided through --input-metadata (through transcribe.py)
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# Outputs NumPy objects containing quantized audio and adequate metadata for use of loading in the trainer through --output-dataset
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"""
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import os
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import argparse
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import torch
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import torchaudio
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import numpy as np
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import logging
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from itertools import combinations
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_logger = logging.getLogger(__name__)
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from tqdm.auto import tqdm
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from pathlib import Path
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import torchaudio.functional as F
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import torchaudio.transforms as T
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from ..config import cfg
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from ..utils import truncate_json
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from ..utils.io import json_read, json_write
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from .g2p import encode as phonemize
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from .qnt import encode as quantize, trim, convert_audio
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from ..webui import init_tts
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def load_audio( path ):
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waveform, sr = torchaudio.load( path )
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# mix channels
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if waveform.shape[0] > 1:
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waveform = torch.mean(waveform, dim=0, keepdim=True)
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# resample
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waveform, sr = convert_audio(waveform, sr, cfg.sample_rate, 1), cfg.sample_rate
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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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text=False,
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audio_backend="encodec",
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device="cuda",
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dtype="float16",
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amp=False,
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verbose=False,
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metadata_path=None,
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top_k=8,
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metadata_keys=[],
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trim_duration=0,
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min_duration=0,
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max_duration=0,
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storage_backend="slop"
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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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cfg.inference.weight_dtype = dtype # "bfloat16"
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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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if tts is None:
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tts = init_tts( config=yaml, restart=False, device=device, dtype=dtype )
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features = { key: None for key in metadata_keys }
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mfcc = None
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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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if not speaker_path.exists():
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return
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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.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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if text:
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if extension not in artifact_extension:
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raise Exception("!")
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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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lang = artifact["metadata"]["language"] if "language" in artifact["metadata"]["language"] else "en"
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if "phonemes" in artifact["metadata"]:
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phn = artifact["metadata"]["phonemes"]
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elif "text" in artifact["metadata"]:
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txt = artifact["metadata"]["text"]
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phn = phonemize( txt, language=lang )
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phn = phn.replace("(en)", "")
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if lang != "en":
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phn = phn.replace(f"({metadata['language']})", "")
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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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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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if trim_duration > 0:
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qnt = trim( qnt, int( cfg.dataset.frames_per_second * trim_duration ) )
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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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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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embedding = mfcc(wav.to(device))[0].t()
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if slop:
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embedding = embedding.sum(dim=0)
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features[filename] = embedding
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# rely on FAISS to handle storing embeddings and handling queries
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# will probably explode in size fast...........
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if storage_backend == "faiss":
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import faiss
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index = faiss.IndexFlatL2( embeddings.shape[-1] )
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embeddings = torch.stack( list( features.values() ) ).cpu()
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index.add( embeddings )
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"""
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# to-do: support just querying for list of similar to cram into JSON metadata
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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.unsqueeze(0).cpu(), k=top_k+1)
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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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return index
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# do batch cosine similarity processing
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keys = list(features.keys())
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top_k = min( top_k, len(keys) )
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if top_k == 0:
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return
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# fill any missing keys with a null embedding to keep the order the same
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null_embedding = torch.zeros( (1024,), device=tts.device, dtype=tts.dtype )
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embeddings = torch.stack( [ feature if feature is not None else null_embedding for feature in features.values() ] )
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sorted_similarities = {}
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for index, filename in tqdm(enumerate(keys), total=len(keys), desc=f"Computing similarities: {speaker_path.name}", disable=not verbose):
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if features[filename] is None:
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continue
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embedding = features[filename].unsqueeze(0)
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similarities = torch.nn.functional.cosine_similarity(embedding, embeddings, dim=1)
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# sorting is slow, don't bother
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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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# set current index to -inf
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similarities[index] = float("-inf")
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topk = torch.topk(similarities, k=top_k, largest=True, sorted=True)
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similarities = [ (index, keys[index], score) for index, score in zip( topk.indices.tolist(), topk.values.tolist() ) ]
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sorted_similarities[filename] = similarities
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return sorted_similarities
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--input-speaker", type=Path, default=None)
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parser.add_argument("--input-voice", type=str, default=None)
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parser.add_argument("--use-dataset", action="store_true")
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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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# dropped, because this might mess with the indices to map to
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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("--top-k", type=int, default=8)
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parser.add_argument("--audio-backend", type=str, default="encodec")
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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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args = parser.parse_args()
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args.skip_existing = True #
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if args.use_dataset:
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cfg.metadata_dir.mkdir(parents=True, exist_ok=True)
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def add( dir, type="training", audios=True, texts=True ):
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name = str(dir)
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name = name.replace(str(cfg.data_dir), "")
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speaker_name = name
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"""
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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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"""
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if args.input_voice and speaker_name != args.input_voice:
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return
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metadata_path = cfg.metadata_dir / f'{speaker_name}.json'
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metadata = json_read( metadata_path, default={} )
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metadata_keys = list(metadata.keys()) if metadata else []
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if args.skip_existing and metadata_keys and "similar" in metadata[metadata_keys[-1]]:
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return
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similarities = process(
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speaker_path=cfg.data_dir / speaker_name,
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yaml=args.yaml,
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text=args.text,
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top_k=args.top_k,
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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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metadata_keys=metadata_keys,
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audio_backend=args.audio_backend,
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device=args.device,
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dtype=args.dtype,
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amp=args.amp,
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verbose=False,
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)
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if not similarities:
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return
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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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for filename, similar in similarities.items():
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if filename not in metadata:
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metadata[filename] = {}
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# overkill but i'm very paranoid about mismatching indices
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metadata[filename]["similar"] = [ metadata_keys.index(s[1]) for s in similar ]
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json_write( metadata, metadata_path )
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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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# validation
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for data_dir in tqdm(sorted(cfg.dataset.validation), desc='Processing Validation'):
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add( data_dir, type="validation" )
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# noise
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for data_dir in tqdm(sorted(cfg.dataset.noise), desc='Processing Noise'):
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add( data_dir, type="noise", texts=False )
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elif args.input_speaker:
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similarities = process(
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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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top_k=args.top_k,
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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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dtype=args.dtype,
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amp=args.amp,
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storage_backend=args.storage_backend,
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verbose=True,
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)
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# and print
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for filename, sim in similarities.items():
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print(f'{filename}: {sim}')
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else:
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raise Exception("!")
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if __name__ == "__main__":
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main() |