mirror of https://github.com/coqui-ai/TTS.git
parent
04a36a727b
commit
a7a96d08dd
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@ -15,8 +15,10 @@
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"hf_url": [
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"https://coqui.gateway.scarf.sh/hf/bark/coarse_2.pt",
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"https://coqui.gateway.scarf.sh/hf/bark/fine_2.pt",
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"https://app.coqui.ai/tts_model/bark_text_2",
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"https://coqui.gateway.scarf.sh/hf/bark/config.json"
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"https://app.coqui.ai/tts_model/text_2.pt",
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"https://coqui.gateway.scarf.sh/hf/bark/config.json",
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"https://coqui.gateway.scarf.sh/hf/bark/hubert.pt",
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"https://coqui.gateway.scarf.sh/hf/bark/tokenizer.pth"
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],
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"default_vocoder": null,
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"commit": "e9a1953e",
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@ -238,7 +240,7 @@
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"tortoise-v2": {
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"description": "Tortoise tts model https://github.com/neonbjb/tortoise-tts",
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"github_rls_url": [
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"https://app.coqui.ai/tts_model/tortoise",
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"https://app.coqui.ai/tts_model/autoregressive.pth",
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"https://coqui.gateway.scarf.sh/v0.14.1_models/clvp2.pth",
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"https://coqui.gateway.scarf.sh/v0.14.1_models/cvvp.pth",
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"https://coqui.gateway.scarf.sh/v0.14.1_models/diffusion_decoder.pth",
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@ -246,6 +246,8 @@ class Bark(BaseTTS):
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text_model_path=None,
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coarse_model_path=None,
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fine_model_path=None,
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hubert_model_path=None,
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hubert_tokenizer_path=None,
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eval=False,
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strict=True,
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**kwargs,
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@ -267,10 +269,14 @@ class Bark(BaseTTS):
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text_model_path = text_model_path or os.path.join(checkpoint_dir, "text_2.pt")
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coarse_model_path = coarse_model_path or os.path.join(checkpoint_dir, "coarse_2.pt")
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fine_model_path = fine_model_path or os.path.join(checkpoint_dir, "fine_2.pt")
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hubert_model_path = hubert_model_path or os.path.join(checkpoint_dir, "hubert.pt")
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hubert_tokenizer_path = hubert_tokenizer_path or os.path.join(checkpoint_dir, "tokenizer.pth")
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self.config.LOCAL_MODEL_PATHS["text"] = text_model_path
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self.config.LOCAL_MODEL_PATHS["coarse"] = coarse_model_path
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self.config.LOCAL_MODEL_PATHS["fine"] = fine_model_path
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self.config.LOCAL_MODEL_PATHS["hubert"] = hubert_model_path
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self.config.LOCAL_MODEL_PATHS["hubert_tokenizer"] = hubert_tokenizer_path
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self.load_bark_models()
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@ -18,7 +18,8 @@ def compute_style_mel(style_wav, ap, cuda=False, device="cpu"):
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if cuda:
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device = "cuda"
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style_mel = torch.FloatTensor(
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ap.melspectrogram(ap.load_wav(style_wav, sr=ap.sample_rate)), device=device,
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ap.melspectrogram(ap.load_wav(style_wav, sr=ap.sample_rate)),
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device=device,
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).unsqueeze(0)
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return style_mel
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