mirror of https://github.com/coqui-ai/TTS.git
73 lines
4.0 KiB
Python
73 lines
4.0 KiB
Python
import os
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import json
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def make_speakers_json_path(out_path):
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"""Returns conventional speakers.json location."""
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return os.path.join(out_path, "speakers.json")
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def load_speaker_mapping(out_path):
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"""Loads speaker mapping if already present."""
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try:
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if os.path.splitext(out_path)[1] == '.json':
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json_file = out_path
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else:
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json_file = make_speakers_json_path(out_path)
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with open(json_file) as f:
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return json.load(f)
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except FileNotFoundError:
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return {}
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def save_speaker_mapping(out_path, speaker_mapping):
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"""Saves speaker mapping if not yet present."""
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speakers_json_path = make_speakers_json_path(out_path)
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with open(speakers_json_path, "w") as f:
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json.dump(speaker_mapping, f, indent=4)
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def get_speakers(items):
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"""Returns a sorted, unique list of speakers in a given dataset."""
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speakers = {e[2] for e in items}
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return sorted(speakers)
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def parse_speakers(c, args, meta_data_train, OUT_PATH):
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""" Returns number of speakers, speaker embedding shape and speaker mapping"""
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if c.use_speaker_embedding:
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speakers = get_speakers(meta_data_train)
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if args.restore_path:
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if c.use_external_speaker_embedding_file: # if restore checkpoint and use External Embedding file
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prev_out_path = os.path.dirname(args.restore_path)
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speaker_mapping = load_speaker_mapping(prev_out_path)
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if not speaker_mapping:
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print("WARNING: speakers.json was not found in restore_path, trying to use CONFIG.external_speaker_embedding_file")
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speaker_mapping = load_speaker_mapping(c.external_speaker_embedding_file)
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if not speaker_mapping:
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raise RuntimeError("You must copy the file speakers.json to restore_path, or set a valid file in CONFIG.external_speaker_embedding_file")
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speaker_embedding_dim = len(speaker_mapping[list(speaker_mapping.keys())[0]]['embedding'])
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elif not c.use_external_speaker_embedding_file: # if restore checkpoint and don't use External Embedding file
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prev_out_path = os.path.dirname(args.restore_path)
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speaker_mapping = load_speaker_mapping(prev_out_path)
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speaker_embedding_dim = None
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assert all([speaker in speaker_mapping
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for speaker in speakers]), "As of now you, you cannot " \
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"introduce new speakers to " \
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"a previously trained model."
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elif c.use_external_speaker_embedding_file and c.external_speaker_embedding_file: # if start new train using External Embedding file
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speaker_mapping = load_speaker_mapping(c.external_speaker_embedding_file)
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speaker_embedding_dim = len(speaker_mapping[list(speaker_mapping.keys())[0]]['embedding'])
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elif c.use_external_speaker_embedding_file and not c.external_speaker_embedding_file: # if start new train using External Embedding file and don't pass external embedding file
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raise "use_external_speaker_embedding_file is True, so you need pass a external speaker embedding file, run GE2E-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb or AngularPrototypical-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb notebook in notebooks/ folder"
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else: # if start new train and don't use External Embedding file
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speaker_mapping = {name: i for i, name in enumerate(speakers)}
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speaker_embedding_dim = None
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save_speaker_mapping(OUT_PATH, speaker_mapping)
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num_speakers = len(speaker_mapping)
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print("Training with {} speakers: {}".format(len(speakers),
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", ".join(speakers)))
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else:
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num_speakers = 0
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speaker_embedding_dim = None
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speaker_mapping = None
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return num_speakers, speaker_embedding_dim, speaker_mapping |