mirror of https://github.com/coqui-ai/TTS.git
Updates BaseTTS and configs
This commit is contained in:
Eren Gölge
parent
176b712c1a
commit
4cd690e4c1
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@ -146,11 +146,19 @@ class BaseTTSConfig(BaseTrainingConfig):
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sort_by_audio_len (bool):
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If true, dataloder sorts the data by audio length else sorts by the input text length. Defaults to `False`.
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min_seq_len (int):
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Minimum sequence length to be used at training.
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min_text_len (int):
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Minimum length of input text to be used. All shorter samples will be ignored. Defaults to 0.
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max_seq_len (int):
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Maximum sequence length to be used at training. Larger values result in more VRAM usage.
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max_text_len (int):
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Maximum length of input text to be used. All longer samples will be ignored. Defaults to float("inf").
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min_audio_len (int):
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Minimum length of input audio to be used. All shorter samples will be ignored. Defaults to 0.
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max_audio_len (int):
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Maximum length of input audio to be used. All longer samples will be ignored. The maximum length in the
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dataset defines the VRAM used in the training. Hence, pay attention to this value if you encounter an
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OOM error in training. Defaults to float("inf").
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compute_f0 (int):
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(Not in use yet).
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@ -211,8 +219,10 @@ class BaseTTSConfig(BaseTrainingConfig):
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loss_masking: bool = None
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# dataloading
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sort_by_audio_len: bool = False
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min_seq_len: int = 1
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max_seq_len: int = float("inf")
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min_audio_len: int = 1
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max_audio_len: int = float("inf")
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min_text_len: int = 1
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max_text_len: int = float("inf")
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compute_f0: bool = False
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compute_linear_spec: bool = False
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use_noise_augment: bool = False
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@ -168,8 +168,8 @@ class BaseTTS(BaseModel):
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Dict: [description]
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"""
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# setup input batch
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text_input = batch["text"]
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text_lengths = batch["text_lengths"]
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text_input = batch["token_id"]
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text_lengths = batch["token_id_lengths"]
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speaker_names = batch["speaker_names"]
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linear_input = batch["linear"]
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mel_input = batch["mel"]
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@ -261,10 +261,6 @@ class BaseTTS(BaseModel):
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d_vector_mapping = None
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# setup custom symbols if needed
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custom_symbols = None
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if hasattr(self, "make_symbols"):
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custom_symbols = self.make_symbols(self.config)
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if hasattr(self, "language_manager"):
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language_id_mapping = (
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self.language_manager.language_id_mapping if self.args.use_language_embedding else None
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@ -282,8 +278,10 @@ class BaseTTS(BaseModel):
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ap=self.ap,
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return_wav=config.return_wav if "return_wav" in config else False,
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batch_group_size=0 if is_eval else config.batch_group_size * config.batch_size,
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min_seq_len=config.min_seq_len,
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max_seq_len=config.max_seq_len,
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min_text_len=config.min_text_len,
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max_text_len=config.max_text_len,
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min_audio_len=config.min_audio_len,
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max_audio_len=config.max_audio_len,
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phoneme_cache_path=config.phoneme_cache_path,
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use_noise_augment=False if is_eval else config.use_noise_augment,
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verbose=verbose,
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@ -292,45 +290,12 @@ class BaseTTS(BaseModel):
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tokenizer=self.tokenizer,
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)
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# pre-compute phonemes
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if config.use_phonemes and config.compute_input_seq_cache and rank in [None, 0]:
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if hasattr(self, "eval_data_items") and is_eval:
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dataset.items = self.eval_data_items
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elif hasattr(self, "train_data_items") and not is_eval:
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dataset.items = self.train_data_items
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else:
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# precompute phonemes for precise estimate of sequence lengths.
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# otherwise `dataset.sort_items()` uses raw text lengths
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dataset.compute_input_seq(config.num_loader_workers)
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# TODO: find a more efficient solution
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# cheap hack - store items in the model state to avoid recomputing when reinit the dataset
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if is_eval:
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self.eval_data_items = dataset.items
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else:
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self.train_data_items = dataset.items
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# halt DDP processes for the main process to finish computing the phoneme cache
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# wait all the DDP process to be ready
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if num_gpus > 1:
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dist.barrier()
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# sort input sequences from short to long
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dataset.sort_and_filter_items(config.get("sort_by_audio_len", default=False))
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# compute pitch frames and write to files.
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if config.compute_f0 and rank in [None, 0]:
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if not os.path.exists(config.f0_cache_path):
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dataset.pitch_extractor.compute_pitch(
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self.ap, config.get("f0_cache_path", None), config.num_loader_workers
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)
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# halt DDP processes for the main process to finish computing the F0 cache
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if num_gpus > 1:
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dist.barrier()
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# load pitch stats computed above by all the workers
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if config.compute_f0:
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dataset.pitch_extractor.load_pitch_stats(config.get("f0_cache_path", None))
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dataset.preprocess_samples()
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# sampler for DDP
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sampler = DistributedSampler(dataset) if num_gpus > 1 else None
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