mirror of https://github.com/coqui-ai/TTS.git
Fix dataset handling with the new embedding file keys (#1991)
Edresson Casanova
GitHub
parent
0a112f7841
commit
3faccbda97
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@ -102,11 +102,9 @@ speaker_mapping = {}
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for idx, fields in enumerate(tqdm(samples)):
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class_name = fields[class_name_key]
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audio_file = fields["audio_file"]
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dataset_name = fields["dataset_name"]
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embedding_key = fields["audio_unique_name"]
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root_path = fields["root_path"]
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relfilepath = os.path.splitext(audio_file.replace(root_path, ""))[0]
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embedding_key = f"{dataset_name}#{relfilepath}"
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if args.old_file is not None and embedding_key in encoder_manager.clip_ids:
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# get the embedding from the old file
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embedd = encoder_manager.get_embedding_by_clip(embedding_key)
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@ -59,6 +59,18 @@ def split_dataset(items, eval_split_max_size=None, eval_split_size=0.01):
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return items[:eval_split_size], items[eval_split_size:]
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def add_extra_keys(metadata, language, dataset_name):
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for item in metadata:
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# add language name
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item["language"] = language
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# add unique audio name
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relfilepath = os.path.splitext(item["audio_file"].replace(item["root_path"], ""))[0]
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audio_unique_name = f"{dataset_name}#{relfilepath}"
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item["audio_unique_name"] = audio_unique_name
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return metadata
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def load_tts_samples(
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datasets: Union[List[Dict], Dict],
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eval_split=True,
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@ -111,15 +123,15 @@ def load_tts_samples(
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# load train set
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meta_data_train = formatter(root_path, meta_file_train, ignored_speakers=ignored_speakers)
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assert len(meta_data_train) > 0, f" [!] No training samples found in {root_path}/{meta_file_train}"
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meta_data_train = [{**item, **{"language": language, "dataset_name": dataset_name}} for item in meta_data_train]
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meta_data_train = add_extra_keys(meta_data_train, language, dataset_name)
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print(f" | > Found {len(meta_data_train)} files in {Path(root_path).resolve()}")
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# load evaluation split if set
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if eval_split:
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if meta_file_val:
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meta_data_eval = formatter(root_path, meta_file_val, ignored_speakers=ignored_speakers)
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meta_data_eval = [
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{**item, **{"language": language, "dataset_name": dataset_name}} for item in meta_data_eval
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]
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meta_data_eval = add_extra_keys(meta_data_eval, language, dataset_name)
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else:
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meta_data_eval, meta_data_train = split_dataset(meta_data_train, eval_split_max_size, eval_split_size)
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meta_data_eval_all += meta_data_eval
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@ -256,6 +256,7 @@ class TTSDataset(Dataset):
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"speaker_name": item["speaker_name"],
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"language_name": item["language"],
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"wav_file_name": os.path.basename(item["audio_file"]),
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"audio_unique_name": item["audio_unique_name"],
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}
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return sample
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@ -397,8 +398,8 @@ class TTSDataset(Dataset):
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language_ids = None
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# get pre-computed d-vectors
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if self.d_vector_mapping is not None:
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wav_files_names = list(batch["wav_file_name"])
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d_vectors = [self.d_vector_mapping[w]["embedding"] for w in wav_files_names]
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embedding_keys = list(batch["audio_unique_name"])
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d_vectors = [self.d_vector_mapping[w]["embedding"] for w in embedding_keys]
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else:
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d_vectors = None
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@ -284,6 +284,7 @@ class VitsDataset(TTSDataset):
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"wav_file": wav_filename,
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"speaker_name": item["speaker_name"],
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"language_name": item["language"],
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"audio_unique_name": item["audio_unique_name"],
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}
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@property
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@ -308,6 +309,7 @@ class VitsDataset(TTSDataset):
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- language_names: :math:`[B]`
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- audiofile_paths: :math:`[B]`
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- raw_texts: :math:`[B]`
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- audio_unique_names: :math:`[B]`
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"""
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# convert list of dicts to dict of lists
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B = len(batch)
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@ -348,6 +350,7 @@ class VitsDataset(TTSDataset):
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"language_names": batch["language_name"],
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"audio_files": batch["wav_file"],
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"raw_text": batch["raw_text"],
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"audio_unique_names": batch["audio_unique_name"],
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}
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@ -1470,7 +1473,7 @@ class Vits(BaseTTS):
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# get d_vectors from audio file names
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if self.speaker_manager is not None and self.speaker_manager.embeddings and self.args.use_d_vector_file:
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d_vector_mapping = self.speaker_manager.embeddings
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d_vectors = [d_vector_mapping[w]["embedding"] for w in batch["audio_files"]]
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d_vectors = [d_vector_mapping[w]["embedding"] for w in batch["audio_unique_names"]]
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d_vectors = torch.FloatTensor(d_vectors)
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# get language ids from language names
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@ -1,5 +1,5 @@
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{
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"LJ001-0001.wav": {
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"#/wavs/LJ001-0001": {
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"name": "ljspeech-0",
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"embedding": [
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0.05539746582508087,
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@ -260,7 +260,7 @@
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-0.09469571709632874
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]
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},
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"LJ001-0002.wav": {
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"#/wavs/LJ001-0002": {
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"name": "ljspeech-1",
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"embedding": [
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0.05539746582508087,
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@ -521,7 +521,7 @@
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-0.09469571709632874
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]
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},
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"LJ001-0003.wav": {
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"#/wavs/LJ001-0003": {
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"name": "ljspeech-2",
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"embedding": [
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0.05539746582508087,
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@ -782,7 +782,7 @@
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-0.09469571709632874
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]
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},
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"LJ001-0004.wav": {
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"#/wavs/LJ001-0004": {
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"name": "ljspeech-3",
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"embedding": [
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0.05539746582508087,
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@ -1043,7 +1043,7 @@
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-0.09469571709632874
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]
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},
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"LJ001-0005.wav": {
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"#/wavs/LJ001-0005": {
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"name": "ljspeech-4",
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"embedding": [
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0.05539746582508087,
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@ -1304,7 +1304,7 @@
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-0.09469571709632874
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]
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},
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"LJ001-0006.wav": {
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"#/wavs/LJ001-0006": {
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"name": "ljspeech-5",
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"embedding": [
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0.05539746582508087,
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@ -1565,7 +1565,7 @@
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-0.09469571709632874
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]
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},
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"LJ001-0007.wav": {
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"#/wavs/LJ001-0007": {
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"name": "ljspeech-6",
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"embedding": [
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0.05539746582508087,
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@ -1826,7 +1826,7 @@
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-0.09469571709632874
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]
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},
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"LJ001-0008.wav": {
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"#/wavs/LJ001-0008": {
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"name": "ljspeech-7",
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"embedding": [
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0.05539746582508087,
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@ -2087,7 +2087,7 @@
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-0.09469571709632874
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]
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},
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"LJ001-0009.wav": {
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"#/wavs/LJ001-0009": {
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"name": "ljspeech-8",
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"embedding": [
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0.05539746582508087,
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@ -2348,7 +2348,7 @@
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-0.09469571709632874
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]
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},
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"LJ001-0010.wav": {
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"#/wavs/LJ001-0010": {
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"name": "ljspeech-9",
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"embedding": [
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0.05539746582508087,
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