mirror of https://github.com/coqui-ai/TTS.git
Merge dataset
This commit is contained in:
Julian WEBER
Eren Gölge
parent
c80cf67d3d
commit
5c89803968
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@ -56,10 +56,6 @@ class TTSDataset(Dataset):
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meta_data (list): List of dataset instances.
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compute_f0 (bool): compute f0 if True. Defaults to False.
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f0_cache_path (str): Path to store f0 cache. Defaults to None.
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characters (dict): `dict` of custom text characters used for converting texts to sequences.
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custom_symbols (list): List of custom symbols used for converting texts to sequences. Models using its own
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@ -109,8 +105,6 @@ class TTSDataset(Dataset):
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self.cleaners = text_cleaner
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self.compute_linear_spec = compute_linear_spec
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self.return_wav = return_wav
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self.compute_f0 = compute_f0
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self.f0_cache_path = f0_cache_path
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self.min_seq_len = min_seq_len
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self.max_seq_len = max_seq_len
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self.ap = ap
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@ -339,7 +333,6 @@ class TTSDataset(Dataset):
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else:
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lengths = np.array([len(ins[0]) for ins in self.items])
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# sort items based on the sequence length in ascending order
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idxs = np.argsort(lengths)
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new_items = []
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ignored = []
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@ -349,10 +342,7 @@ class TTSDataset(Dataset):
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ignored.append(idx)
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else:
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new_items.append(self.items[idx])
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# shuffle batch groups
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# create batches with similar length items
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# the larger the `batch_group_size`, the higher the length variety in a batch.
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if self.batch_group_size > 0:
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for i in range(len(new_items) // self.batch_group_size):
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offset = i * self.batch_group_size
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@ -360,14 +350,8 @@ class TTSDataset(Dataset):
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temp_items = new_items[offset:end_offset]
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random.shuffle(temp_items)
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new_items[offset:end_offset] = temp_items
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if len(new_items) == 0:
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raise RuntimeError(" [!] No items left after filtering.")
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# update items to the new sorted items
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self.items = new_items
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# logging
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if self.verbose:
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print(" | > Max length sequence: {}".format(np.max(lengths)))
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print(" | > Min length sequence: {}".format(np.min(lengths)))
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@ -554,110 +538,3 @@ class TTSDataset(Dataset):
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)
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)
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)
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class PitchExtractor:
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"""Pitch Extractor for computing F0 from wav files.
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Args:
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items (List[List]): Dataset samples.
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verbose (bool): Whether to print the progress.
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"""
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def __init__(
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self,
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items: List[List],
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verbose=False,
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):
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self.items = items
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self.verbose = verbose
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self.mean = None
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self.std = None
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@staticmethod
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def create_pitch_file_path(wav_file, cache_path):
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file_name = os.path.splitext(os.path.basename(wav_file))[0]
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pitch_file = os.path.join(cache_path, file_name + "_pitch.npy")
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return pitch_file
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@staticmethod
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def _compute_and_save_pitch(ap, wav_file, pitch_file=None):
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wav = ap.load_wav(wav_file)
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pitch = ap.compute_f0(wav)
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if pitch_file:
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np.save(pitch_file, pitch)
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return pitch
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@staticmethod
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def compute_pitch_stats(pitch_vecs):
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nonzeros = np.concatenate([v[np.where(v != 0.0)[0]] for v in pitch_vecs])
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mean, std = np.mean(nonzeros), np.std(nonzeros)
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return mean, std
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def normalize_pitch(self, pitch):
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zero_idxs = np.where(pitch == 0.0)[0]
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pitch = pitch - self.mean
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pitch = pitch / self.std
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pitch[zero_idxs] = 0.0
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return pitch
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def denormalize_pitch(self, pitch):
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zero_idxs = np.where(pitch == 0.0)[0]
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pitch *= self.std
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pitch += self.mean
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pitch[zero_idxs] = 0.0
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return pitch
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@staticmethod
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def load_or_compute_pitch(ap, wav_file, cache_path):
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"""
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compute pitch and return a numpy array of pitch values
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"""
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pitch_file = PitchExtractor.create_pitch_file_path(wav_file, cache_path)
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if not os.path.exists(pitch_file):
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pitch = PitchExtractor._compute_and_save_pitch(ap, wav_file, pitch_file)
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else:
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pitch = np.load(pitch_file)
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return pitch.astype(np.float32)
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@staticmethod
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def _pitch_worker(args):
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item = args[0]
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ap = args[1]
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cache_path = args[2]
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_, wav_file, *_ = item
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pitch_file = PitchExtractor.create_pitch_file_path(wav_file, cache_path)
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if not os.path.exists(pitch_file):
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pitch = PitchExtractor._compute_and_save_pitch(ap, wav_file, pitch_file)
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return pitch
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return None
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def compute_pitch(self, ap, cache_path, num_workers=0):
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"""Compute the input sequences with multi-processing.
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Call it before passing dataset to the data loader to cache the input sequences for faster data loading."""
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if not os.path.exists(cache_path):
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os.makedirs(cache_path, exist_ok=True)
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if self.verbose:
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print(" | > Computing pitch features ...")
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if num_workers == 0:
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pitch_vecs = []
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for _, item in enumerate(tqdm.tqdm(self.items)):
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pitch_vecs += [self._pitch_worker([item, ap, cache_path])]
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else:
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with Pool(num_workers) as p:
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pitch_vecs = list(
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tqdm.tqdm(
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p.imap(PitchExtractor._pitch_worker, [[item, ap, cache_path] for item in self.items]),
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total=len(self.items),
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)
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)
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pitch_mean, pitch_std = self.compute_pitch_stats(pitch_vecs)
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pitch_stats = {"mean": pitch_mean, "std": pitch_std}
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np.save(os.path.join(cache_path, "pitch_stats"), pitch_stats, allow_pickle=True)
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def load_pitch_stats(self, cache_path):
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stats_path = os.path.join(cache_path, "pitch_stats.npy")
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stats = np.load(stats_path, allow_pickle=True).item()
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self.mean = stats["mean"].astype(np.float32)
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self.std = stats["std"].astype(np.float32)
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