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