dataset preparation script updates, caved and am using HF tokenizer now
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137
data/config.yaml
137
data/config.yaml
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@ -1,51 +1,23 @@
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dataset:
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training: []
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validation: []
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noise: []
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speaker_name_getter: "lambda p: f'{p.parts[-3]}_{p.parts[-2]}'"
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use_hdf5: True
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use_metadata: True
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hdf5_flag: r
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validate: True
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workers: 2
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cache: True
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phones_range: [4, 512]
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duration_range: [1.0, 32.0]
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random_utterance: 1.0
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max_prompts: 3
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prompt_duration: 6.0
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sample_type: speaker
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tasks_list: [ "tts" ] # , [ "tts", "tts-c", "ns", "sr", "tse", "cse", "nse", "tts"]
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models:
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_prom_levels: 8
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_max_levels: 8
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_models:
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- name: "ar+nar"
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size: "full"
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resp_levels: 8
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prom_levels: 8
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tasks: 8
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arch_type: "retnet"
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training: True
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version: 3
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- name: "ar+nar"
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size: "full"
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resp_levels: 8
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prom_levels: 8
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tasks: 8
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langs: 2
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tones: 1
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arch_type: "retnet"
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training: True
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version: 3
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hyperparameters:
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batch_size: 8
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gradient_accumulation_steps: 32
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gradient_clipping: 100
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batch_size: 4
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gradient_accumulation_steps: 4
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gradient_clipping: 10
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optimizer: Prodigy
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optimizer: Adagrad
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torch_optimizer: True
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learning_rate: 0.0625
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learning_rate: 1.0e-2
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scheduler_type: ""
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#scheduler_type: OneCycle
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@ -67,22 +39,24 @@ hyperparameters:
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# decay_mom_rate: 0.0
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evaluation:
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batch_size: 16
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frequency: 250
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size: 16
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batch_size: 8
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frequency: 10000
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size: 8
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steps: 450
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steps: 500
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ar_temperature: 0.95
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nar_temperature: 0.25
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load_disabled_engines: True
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trainer:
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no_logger: True
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iterations: 1_000_000
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save_tag: step
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save_on_oom: True
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save_on_quit: True
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save_frequency: 100
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save_frequency: 250
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export_on_save: True
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keep_last_checkpoints: 4
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@ -91,33 +65,82 @@ trainer:
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load_disabled_engines: False
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#load_state_dict: True
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#strict_loading: False
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strict_loading: False
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#load_tag: "9500"
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#load_states: False
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#restart_step_count: True
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gc_mode: None # "global_step"
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weight_dtype: bfloat16
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weight_dtype: float32
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amp: False
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backend: deepspeed
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deepspeed:
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inferencing: True
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zero_optimization_level: 0
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use_compression_training: True
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use_compression_training: False
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activation_checkpointing: True
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load_webui: True
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inference:
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use_vocos: True
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backend: deepspeed
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audio_backend: "dac"
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normalize: False
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weight_dtype: bfloat16
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weight_dtype: float32
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amp: False
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bitsandbytes:
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enabled: False
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injects: True
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linear: True
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embedding: True
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injects: False
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replace: False
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linear: False
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embedding: False
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bitnet: False
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fp8:
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enabled: False
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backend: "te"
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experimental: True
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dataset:
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speaker_name_getter: "lambda p: f'{p.parts[-3]}_{p.parts[-2]}'"
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speaker_group_getter: "lambda p: f'{p.parts[-3]}'"
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speaker_languages:
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ja: []
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use_hdf5: True
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use_metadata: True
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hdf5_flag: r
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validate: True
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workers: 8
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cache: True
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#phones_range: [4, 512]
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#duration_range: [1.0, 32.0]
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phones_range: [0, 512]
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duration_range: [0.0, 64.0]
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random_utterance: 1.0
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max_prompts: 3
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prompt_duration: 6.0
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max_resps: 1
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p_resp_append: 0.25
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sample_type: speaker
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tasks_list: [ "tts" ] # , [ "tts", "tts-c", "ns", "sr", "tse", "cse", "nse", "tts"]
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training: []
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validation: []
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noise: []
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BIN
data/qnt.dac.pt
BIN
data/qnt.dac.pt
Binary file not shown.
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@ -1,8 +1,8 @@
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import os
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import json
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input_dataset = "small+medium"
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output_dataset = "LibriLight-6K"
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input_dataset = "duplicate"
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output_dataset = "LibriLight-4K"
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for speaker_id in os.listdir(f'./{input_dataset}/'):
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if not os.path.isdir(f'./{input_dataset}/{speaker_id}/'):
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@ -8,9 +8,14 @@ from pathlib import Path
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from vall_e.emb.g2p import encode as valle_phonemize
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from vall_e.emb.qnt import encode as valle_quantize, _replace_file_extension
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input_audio = "voices"
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input_audio = "voice"
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input_metadata = "metadata"
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output_dataset = "training"
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output_dataset = "training-24K"
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missing = {
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"transcription": [],
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"audio": []
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}
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device = "cuda"
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@ -31,13 +36,15 @@ for dataset_name in os.listdir(f'./{input_audio}/'):
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metadata_path = Path(f'./{input_metadata}/{dataset_name}/{speaker_id}/whisper.json')
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if not metadata_path.exists():
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print("Does not exist:", metadata_path)
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#print("Does not exist:", metadata_path)
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missing["transcription"].append(str(metadata_path))
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continue
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try:
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metadata = json.loads(open(metadata_path, "r", encoding="utf-8").read())
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except Exception as e:
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print("Failed to load metadata:", metadata_path, e)
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#print("Failed to load metadata:", metadata_path, e)
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missing["transcription"].append(str(metadata_path))
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continue
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txts = []
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@ -46,7 +53,8 @@ for dataset_name in os.listdir(f'./{input_audio}/'):
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for filename in metadata.keys():
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inpath = Path(f'./{input_audio}/{dataset_name}/{speaker_id}/{filename}')
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if not inpath.exists():
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print("Does not exist:", inpath)
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#print("Does not exist:", inpath)
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missing["audio"].append(str(inpath))
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continue
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extension = os.path.splitext(filename)[-1][1:]
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@ -117,21 +125,26 @@ for dataset_name in os.listdir(f'./{input_audio}/'):
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waveform[:, start:end],
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sample_rate
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))
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for job in tqdm(txts, desc=f"Phonemizing: {speaker_id}"):
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outpath, text, language = job
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phones = valle_phonemize(text)
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data = {
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"text": text.strip(),
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"phonemes": phones,
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"language": language,
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}
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open(_replace_file_extension(outpath, ".json"), 'w', encoding='utf-8').write(json.dumps(data))
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for job in tqdm(wavs, desc=f"Quantizing: {speaker_id}"):
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try:
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outpath, waveform, sample_rate = job
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qnt = valle_quantize(waveform, sr=sample_rate, device=device)
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qnt.save(_replace_file_extension(outpath, ".dac"))
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except Exception as e:
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print(f"Failed to quantize: {outpath}:", e)
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continue
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if len(txts) > 0:
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for job in tqdm(txts, desc=f"Phonemizing: {speaker_id}"):
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outpath, text, language = job
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phones = valle_phonemize(text)
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data = {
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"text": text.strip(),
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"phonemes": phones,
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"language": language,
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}
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open(_replace_file_extension(outpath, ".json"), 'w', encoding='utf-8').write(json.dumps(data))
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if len(wavs) > 0:
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for job in tqdm(wavs, desc=f"Quantizing: {speaker_id}"):
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try:
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outpath, waveform, sample_rate = job
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qnt = valle_quantize(waveform, sr=sample_rate, device=device)
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qnt.save(_replace_file_extension(outpath, ".dac"))
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except Exception as e:
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print(f"Failed to quantize: {outpath}:", e)
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continue
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open("./missing.json", 'w', encoding='utf-8').write(json.dumps(missing))
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57
scripts/train_tokenizer.py
Normal file
57
scripts/train_tokenizer.py
Normal file
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@ -0,0 +1,57 @@
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import os
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import json
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import torch
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import torchaudio
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from tqdm.auto import tqdm
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from pathlib import Path
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from tokenizers import Tokenizer
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from tokenizers.models import BPE, Unigram, WordLevel, WordPiece
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from tokenizers.trainers import BpeTrainer
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from tokenizers.pre_tokenizers import Whitespace
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from tokenizers.processors import TemplateProcessing
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input_metadata = "training-24K"
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output_file = Path("./dataset.json")
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tokenizer_data = []
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def pad(num, zeroes):
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return str(num).zfill(zeroes+1)
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if output_file.exists():
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tokenizer_data = json.loads(open(str(output_file), "r", encoding="utf-8").read())
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else:
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for dataset_name in os.listdir(f'./{input_metadata}/'):
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if not os.path.isdir(f'./{input_metadata}/{dataset_name}/'):
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continue
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for speaker_id in tqdm(os.listdir(f'./{input_metadata}/{dataset_name}/'), desc="Processing speaker"):
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if not os.path.isdir(f'./{input_metadata}/{dataset_name}/{speaker_id}'):
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continue
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for id in os.listdir(f'./{input_metadata}/{dataset_name}/{speaker_id}/'):
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if ".json" not in id:
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continue
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metadata_path = Path(f'./{input_metadata}/{dataset_name}/{speaker_id}/{id}')
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metadata = json.loads(open(metadata_path, "r", encoding="utf-8").read())
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tokenizer_data.append( f'{"".join(metadata["phonemes"])}' )
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open(output_file, 'w', encoding='utf-8').write(json.dumps(tokenizer_data))
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unk_token = "<unk>"
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spl_tokens = ["<bos>", "</eos>", unk_token, "<mask>"]
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trainer = BpeTrainer(special_tokens = spl_tokens, vocab_size = 256)
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tokenizer = Tokenizer(BPE(unk_token = unk_token))
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tokenizer.pre_tokenizer = Whitespace()
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tokenizer.post_processor = TemplateProcessing(
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single="<bos> $A <eos>",
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special_tokens=[("<bos>", 1), ("<eos>", 2)],
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)
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tokenizer.train_from_iterator(tokenizer_data, trainer=trainer)
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tokenizer.save("./tokenizer.json")
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@ -18,6 +18,9 @@ from omegaconf import OmegaConf
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from .utils.distributed import world_size
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# Yuck
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from transformers import PreTrainedTokenizerFast
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@dataclass()
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class _Config:
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cfg_path: str | None = None
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@ -540,10 +543,12 @@ class Config(_Config):
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inference: Inference = field(default_factory=lambda: Inference)
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bitsandbytes: BitsAndBytes = field(default_factory=lambda: BitsAndBytes)
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tokenizer: str = "./tokenizer.json"
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fp8: FP8 = field(default_factory=lambda: FP8)
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sample_rate: int = 24_000
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variable_sample_rate: bool = False
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variable_sample_rate: bool = True
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@property
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def distributed(self):
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@ -611,16 +616,19 @@ cfg = Config.from_cli()
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# OmegaConf might not coerce the dicts into the @dataclass decorated classes, so we (try to) coerce them ourselves
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try:
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cfg.format()
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# cached_property stopped working...
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if cfg.dataset.use_hdf5:
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cfg.load_hdf5()
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except Exception as e:
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print(e)
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print("Error while parsing config YAML:", e)
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pass
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try:
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from transformers import PreTrainedTokenizerFast
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cfg.tokenizer = (cfg.relpath if cfg.cfg_path is not None else Path("./data/")) / cfg.tokenizer
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cfg.tokenizer = PreTrainedTokenizerFast(tokenizer_file=str(cfg.tokenizer))
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except Exception as e:
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print("Error while parsing tokenizer:", e)
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pass
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if __name__ == "__main__":
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print(cfg)
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@ -24,17 +24,17 @@ from torch import Tensor
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from torch.utils.data import DataLoader, Dataset as _Dataset
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from torch.utils.data.distributed import DistributedSampler
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from tqdm.auto import tqdm
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# torch.multiprocessing.set_sharing_strategy("file_system")
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_logger = logging.getLogger(__name__)
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# to-do: clean up this symmap mess
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def get_phone_symmap():
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if cfg.dataset.use_hdf5 and 'symmap' in cfg.hdf5:
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return json.loads( cfg.hdf5['symmap'].asstr()[()] )
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return cfg.tokenizer.get_vocab()
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return {'<s>': 1, '</s>': 2, ' ': 3, '.': 4, ',': 5, '!': 6, '?': 7, 'p': 7, 'iː': 8, 'ɚ': 9, 'ˌ': 10, 'dˌ': 11, 'mˌ': 12, 'd': 13, 'ɹ': 14, 'tˈ': 15, 'pˌ': 16, 'uː': 17, 'l': 18, 'æ': 19, 'ɛ': 20, 'ɪ': 21, 'j': 22, 'ʊ': 23, 't': 24, 'n': 25, 'v': 26, 'a': 27, 'o': 28, 'ŋ': 29, 'w': 30, 'ʌ': 31, 'hˈ': 32, 'ɡˈ': 33, 'ə': 34, 'θˈ': 35, 'dˈ': 36, 'wˌ': 37, 'h': 38, 'z': 39, 'k': 40, 'ð': 41, 'ɡˌ': 42, 'ˈ': 43, 'fˈ': 44, 'i': 45, 's': 46, 'ʃ': 47, 'wˈ': 48, 'ðˈ': 49, 'ɹˈ': 50, 'lˈ': 51, 'ɡ': 52, 'oː': 53, 'mˈ': 54, 'e': 55, 'ɑː': 56, 'nˈ': 57, 'm': 58, 'θˌ': 59, 'sˈ': 60, 'f': 61, 'ɔː': 62, 'hˌ': 63, 'b': 64, 'jˈ': 65, 'ɐ': 66, 'ʒˈ': 67, 'θ': 68, 'bˈ': 69, 'ɾ': 70, 'ɜː': 71, 'ʌˈ': 72, 'ʃˌ': 73, 'bˌ': 74, 'kˈ': 75, 'ɔ': 76, 'zˈ': 77, 'ᵻ': 78, 'kˌ': 79, 'vˈ': 80, 'fˌ': 81, 'ʒ': 82, 'ʃˈ': 83, 'ɹˌ': 84, 'tˌ': 85, 'pˈ': 86, 'ðˌ': 87, 'sˌ': 88, 'nˌ': 89, 'lˌ': 90, '̩': 91, 'ʔ': 92, 'vˌ': 93, 'ɪˈ': 94, '"': 95, 'ɪˌ': 96, 'ʒˌ': 97, 'uːˌ': 98, 'ʊˈ': 99, 'jˌ': 100, 'uːˈ': 101, 'iːˈ': 102, 'zˌ': 103, '.ˈ': 104, '…': 105, 'ŋˌ': 106, 'ɐˌ': 107, '—ˈ': 108, 'iˌ': 109, 'iːˌ': 110, 'ɛː': 111, ')': 112, ')ˈ': 113, '(': 114, 'u': 115, '-': 116, 'ɖˈ': 117, 'iˈ': 118, 'ʰˈ': 119, 'ɟˈ': 120, '̃': 121, 'eː': 122, 'ɾˈ': 123, 'r': 124, 'ʰ': 125, '-ˌ': 126, 'ɫ': 127, 'q': 128, '—': 129, 'ʊˌ': 130, 'aː': 131, 'cˈ': 132, '…ˈ': 133, 'c': 134, 'ɳ': 135, 'ɐˈ': 136, 'x': 137, 'ʔˌ': 138, '.ˌ': 139, 'ɑ': 140, '?ˈ': 141, '̩ˈ': 142, '"ˈ': 143, ',ˈ': 144, 'ŋˈ': 145, 'əˌ': 146, '!ˈ': 147, '"ˌ': 148, '?ˌ': 149, ',ˌ': 150, '—ˌ': 151, '̩ˌ': 152, 'əˈ': 153, '!ˌ': 154, 'ɬ': 155, 'ʲ': 156, '¡': 157, 'ɯ': 158, 'qˌ': 159, 'ʑ': 160, 'ʑˈ': 161, '¿': 162, 'ɑːˈ': 163, 'iːː': 164, 'ɛˈ': 165, '¡ˈ': 166, 'æˈ': 167, 'ç': 168, 'ɾˌ': 169, 'ᵻˈ': 170, 'xˈ': 171, 'ɔːˈ': 172, ';': 173, 'ɬˌ': 174, ':': 175, 'ʔˈ': 176, 'ɑːˌ': 177, 'ɬˈ': 178, '”': 179, '“': 180, '“ˈ': 181, '“ˌ': 182, ';ˈ': 183, ';ˌ': 184, ':ˈ': 185, '1': 186, 'rˈ': 187, 'qˈ': 188, 'ᵻˌ': 189, 'ä': 190, '̞ˌ': 191, '̞': 192, 'ũˌ': 193, 'ʑˌ': 194, 'ᵝ': 195, 'ɽ': 196, 'ʲˌ': 197, 'ᵝˌ': 198, 'ũ': 199, 'ũˈ': 200, 'äˌ': 201, 'ɕ': 202, 'ɕˌ': 203, 'ɽˌ': 204, 'çˌ': 205, '…ˌ': 206, '̞ˈ': 207, 'äˈ': 208, 'ɽˈ': 209, 'ɸˌ': 210, 'ɴ': 211, 'ɸˈ': 212, 'ɕˈ': 213, 'ɸ': 214, 'ᵝˈ': 215, 'ʲˈ': 216, 'ĩ': 217, 'çˈ': 218, 'ĩˌ': 219, 'oˌ': 220, 'eˈ': 221, 'ʍ': 222, 'eˌ': 223, 'uˌ': 224, 'ʍˌ': 225, 'uˈ': 226, 'oˈ': 227, 'aˈ': 228}
|
||||
def tokenize( phones ):
|
||||
return tokenizer.encode( "".join(phones) )
|
||||
#return [*map(get_phone_symmap.get, _get_phones(path))]
|
||||
|
||||
def get_lang_symmap():
|
||||
return {
|
||||
|
|
@ -178,7 +178,9 @@ def _get_phones(path, language="en"):
|
|||
else:
|
||||
content = open(_get_phone_path(path), "r", encoding="utf-8").read().split(" ")
|
||||
content = _cleanup_phones( content )
|
||||
return ["<s>"] + [ " " if not p else p for p in content ] + ["</s>"]
|
||||
|
||||
return "".join(content)
|
||||
#return ["<s>"] + [ " " if not p else p for p in content ] + ["</s>"]
|
||||
|
||||
def _interleaved_reorder(l, fn):
|
||||
groups = defaultdict(list)
|
||||
|
|
@ -435,7 +437,7 @@ class Dataset(_Dataset):
|
|||
text = torch.from_numpy(text).to(self.text_dtype)
|
||||
resps = torch.from_numpy(resps).to(torch.int16)
|
||||
else:
|
||||
text = torch.tensor([*map(self.phone_symmap.get, _get_phones(path))]).to(self.text_dtype)
|
||||
text = torch.tensor(tokenize( _get_phones( path ) )).to(self.text_dtype)
|
||||
resps = _load_quants(path)
|
||||
|
||||
lang = torch.tensor([ self.lang_symmap[ self.get_language(spkr_group) ]]).to(torch.uint8)
|
||||
|
|
@ -847,18 +849,21 @@ def create_dataset_hdf5( skip_existing=True ):
|
|||
# audio
|
||||
if audios:
|
||||
qnt = np.load(f'{root}/{name}/{id}{_get_quant_extension()}', allow_pickle=True)[()]
|
||||
codes = torch.from_numpy(qnt["codes"].astype(int))[0].t()
|
||||
codes = torch.from_numpy(qnt["codes"].astype(int))[0].t().to(dtype=torch.int16)
|
||||
|
||||
if _get_quant_extension() == ".dac":
|
||||
if "audio" in group:
|
||||
del group["audio"]
|
||||
duration = qnt["metadata"]["original_length"] / qnt["metadata"]["sample_rate"]
|
||||
metadata[id]["metadata"] = qnt["metadata"]
|
||||
metadata[id]["metadata"] = {
|
||||
"original_length": qnt["metadata"]["original_length"],
|
||||
"sample_rate": qnt["metadata"]["sample_rate"],
|
||||
}
|
||||
else:
|
||||
qnt = torch.load(f'{root}/{name}/{id}{_get_quant_extension()}')[0].t()
|
||||
duration = qnt.shape[0] / 75
|
||||
|
||||
group.create_dataset('audio', data=qnt.numpy(), compression='lzf')
|
||||
group.create_dataset('audio', data=qnt.numpy().astype(np.int16), compression='lzf')
|
||||
|
||||
group.attrs['duration'] = duration
|
||||
metadata[id]["duration"] = duration
|
||||
|
|
@ -869,17 +874,22 @@ def create_dataset_hdf5( skip_existing=True ):
|
|||
# text
|
||||
if texts:
|
||||
if _get_quant_extension() == ".json":
|
||||
j_son = json.loads(open(f'{root}/{name}/{id}{_get_phone_extension()}', "r", encoding="utf-8").read())
|
||||
content = j_son["phonemes"]
|
||||
json_metadata = json.loads(open(f'{root}/{name}/{id}{_get_phone_extension()}', "r", encoding="utf-8").read())
|
||||
content = json_metadata["phonemes"]
|
||||
else:
|
||||
content = open(f'{root}/{name}/{id}{_get_phone_extension()}', "r", encoding="utf-8").read().split(" ")
|
||||
|
||||
"""
|
||||
phones = [f"<s>"] + [ " " if not p else p for p in content ] + [f"</s>"]
|
||||
for s in set(phones):
|
||||
if s not in symmap:
|
||||
symmap[s] = len(symmap.keys())
|
||||
|
||||
phn = [ symmap[s] for s in phones ]
|
||||
"""
|
||||
|
||||
phn = cfg.tokenizer.encode("".join(content))
|
||||
phn = np.array(phn).astype(np.uint8)
|
||||
|
||||
if "text" in group:
|
||||
del group["text"]
|
||||
|
|
|
|||
|
|
@ -91,15 +91,8 @@ class TTS():
|
|||
return text
|
||||
|
||||
content = g2p.encode(text, language=language)
|
||||
content = _cleanup_phones( content )
|
||||
# ick
|
||||
try:
|
||||
phones = ["<s>"] + [ " " if not p else p for p in content ] + ["</s>"]
|
||||
return torch.tensor([*map(self.symmap.get, phones)])
|
||||
except Exception as e:
|
||||
pass
|
||||
phones = [ " " if not p else p for p in content ]
|
||||
return torch.tensor([ 1 ] + [*map(self.symmap.get, phones)] + [ 2 ])
|
||||
|
||||
return torch.tensor(cfg.tokenizer.encode( "".join( content ) ))
|
||||
|
||||
def encode_lang( self, language ):
|
||||
symmap = get_lang_symmap()
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user