Medix-AI/coqui-tts
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coqui-tts
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Add support for inference using an specific reference file instead of the averaged embeddings

This commit is contained in:
Edresson Casanova 2022-06-13 13:47:31 +00:00
parent 856e185641
commit 251e1c289d
2 changed files with 31 additions and 11 deletions
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34
TTS/utils/synthesizer.py
View File

@ -252,17 +252,24 @@ class Synthesizer(object):
if speaker_name and isinstance(speaker_name, str):
if self.tts_config.use_d_vector_file:
# get the average speaker embedding from the saved d_vectors.
speaker_embedding = self.tts_model.speaker_manager.get_mean_embedding(
speaker_name, num_samples=None, randomize=False
)
if speaker_name in self.tts_model.speaker_manager.ids:
speaker_embedding = self.tts_model.speaker_manager.get_mean_embedding(
speaker_name, num_samples=None, randomize=False
)
else:
speaker_embedding = self.tts_model.speaker_manager.embeddings[speaker_name]["embedding"]
speaker_embedding = np.array(speaker_embedding)[None, :] # [1 x embedding_dim]
if style_speaker_name is not None:
style_speaker_embedding = self.tts_model.speaker_manager.get_mean_embedding(
style_speaker_name, num_samples=None, randomize=False
)
style_speaker_embedding = np.array(style_speaker_embedding)[None, :] # [1 x embedding_dim]
if style_speaker_name in self.tts_model.speaker_manager.ids:
style_speaker_embedding = self.tts_model.speaker_manager.get_mean_embedding(
style_speaker_name, num_samples=None, randomize=False
)
else:
style_speaker_embedding = self.tts_model.speaker_manager.embeddings[style_speaker_name]["embedding"]
style_speaker_embedding = np.array(style_speaker_embedding)[None, :] # [1 x embedding_dim]
else:
# get speaker idx from the speaker name
speaker_id = self.tts_model.speaker_manager.ids[speaker_name]
@ -322,11 +329,16 @@ class Synthesizer(object):
getattr(self.tts_config, "model_args", None)
and getattr(self.tts_config.model_args, "use_external_emotions_embeddings", False)
):
# get the average speaker embedding from the saved embeddings.
emotion_embedding = self.tts_model.emotion_manager.get_mean_embedding(
emotion_name, num_samples=None, randomize=False
)
if emotion_name in self.tts_model.emotion_manager.ids:
# get the average speaker embedding from the saved embeddings.
emotion_embedding = self.tts_model.emotion_manager.get_mean_embedding(
emotion_name, num_samples=None, randomize=False
)
else:
emotion_embedding = self.tts_model.emotion_manager.embeddings[emotion_name]["embedding"]
emotion_embedding = np.array(emotion_embedding)[None, :] # [1 x embedding_dim]
else:
# get speaker idx from the speaker name
emotion_id = self.tts_model.emotion_manager.ids[emotion_name]

8
tests/tts_tests/test_vits_speaker_emb_with_emotion_train.py
View File

@ -27,6 +27,7 @@ config = VitsConfig(
print_eval=True,
test_sentences=[
["Be a voice, not an echo.", "ljspeech-1", None, None, "ljspeech-1"],
["Be a voice, not an echo.", "LJ001-0001.wav", None, None, "LJ001-0002.wav"],
],
)
# set audio config
@ -83,6 +84,13 @@ continue_emotion_path = os.path.join(continue_path, "emotions.json")
inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --emotion_idx {emotion_id} --speakers_file_path {continue_speakers_path} --emotions_file_path {continue_emotion_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
run_cli(inference_command)
# inference with one reference embedding instead of average
speaker_id = "LJ001-0001.wav"
emotion_id = "LJ001-0002.wav"
inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --emotion_idx {emotion_id} --speakers_file_path {continue_speakers_path} --emotions_file_path {continue_emotion_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
run_cli(inference_command)
# restore the model and continue training for one more epoch
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
run_cli(command_train)