Medix-AI/coqui-tts
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coqui-tts
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remove conflicy noise

This commit is contained in:
Eren Gölge 2021-04-26 15:27:52 +02:00
parent f37b488876
commit b531fa699c
1 changed files with 0 additions and 42 deletions
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42
TTS/tts/utils/speakers.py
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@ -133,10 +133,6 @@ class SpeakerManager:
encoder_model_path (str, optional): Path to the speaker encoder model file. Defaults to "".
encoder_config_path (str, optional): Path to the spealer encoder config file. Defaults to "".
"""
<<<<<<< HEAD
=======
>>>>>>> 757dfb9289c7185b0b78d2aa75e8a0c9b2911777
def __init__(
self,
x_vectors_file_path: str = "",
@ -149,10 +145,7 @@ class SpeakerManager:
self.speaker_ids = None
self.clip_ids = None
self.speaker_encoder = None
<<<<<<< HEAD
self.speaker_encoder_ap = None
=======
>>>>>>> 757dfb9289c7185b0b78d2aa75e8a0c9b2911777
if x_vectors_file_path:
self.load_x_vectors_file(x_vectors_file_path)
@ -198,51 +191,23 @@ class SpeakerManager:
def load_x_vectors_file(self, file_path: str):
self.x_vectors = self._load_json(file_path)
<<<<<<< HEAD
self.speaker_ids = list(set(sorted(x["name"] for x in self.x_vectors.values())))
self.clip_ids = list(set(sorted(clip_name for clip_name in self.x_vectors.keys())))
=======
self.speaker_ids = list(
set(sorted(x["name"] for x in self.x_vectors.values())))
self.clip_ids = list(
set(sorted(clip_name for clip_name in self.x_vectors.keys())))
>>>>>>> 757dfb9289c7185b0b78d2aa75e8a0c9b2911777
def get_x_vector_by_clip(self, clip_idx: str):
return self.x_vectors[clip_idx]["embedding"]
def get_x_vectors_by_speaker(self, speaker_idx: str):
<<<<<<< HEAD
return [x["embedding"] for x in self.x_vectors.values() if x["name"] == speaker_idx]
def get_mean_x_vector(self, speaker_idx: str, num_samples: int = None, randomize: bool = False):
=======
return [
x["embedding"] for x in self.x_vectors.values()
if x["name"] == speaker_idx
]
def get_mean_x_vector(self,
speaker_idx: str,
num_samples: int = None,
randomize: bool = False):
>>>>>>> 757dfb9289c7185b0b78d2aa75e8a0c9b2911777
x_vectors = self.get_x_vectors_by_speaker(speaker_idx)
if num_samples is None:
x_vectors = np.stack(x_vectors).mean(0)
else:
<<<<<<< HEAD
assert len(x_vectors) >= num_samples, f" [!] speaker {speaker_idx} has number of samples < {num_samples}"
if randomize:
x_vectors = np.stack(random.choices(x_vectors, k=num_samples)).mean(0)
=======
assert len(
x_vectors
) >= num_samples, f" [!] speaker {speaker_idx} has number of samples < {num_samples}"
if randomize:
x_vectors = np.stack(random.choices(x_vectors,
k=num_samples)).mean(0)
>>>>>>> 757dfb9289c7185b0b78d2aa75e8a0c9b2911777
else:
x_vectors = np.stack(x_vectors[:num_samples]).mean(0)
return x_vectors
@ -253,7 +218,6 @@ class SpeakerManager:
def get_clips(self):
return sorted(self.x_vectors.keys())
<<<<<<< HEAD
def init_speaker_encoder(self, model_path: str, config_path: str) -> None:
self.speaker_encoder_config = load_config(config_path)
self.speaker_encoder = setup_model(self.speaker_encoder_config)
@ -284,12 +248,6 @@ class SpeakerManager:
return (x_vectors / len(wav_file))[0].tolist()
x_vector = _compute(wav_file)
return x_vector[0].tolist()
=======
def init_speaker_encoder(self, model_path: str, config_path: str):
self.speaker_encoder_config = load_config(config_path)
self.speaker_encoder = setup_model(self.speaker_encoder_config)
self.speaker_encoder.load_checkpoint(config_path, model_path, True)
>>>>>>> 757dfb9289c7185b0b78d2aa75e8a0c9b2911777
def compute_x_vector(self, feats):
if isinstance(feats, np.ndarray):