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Update app.py
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app.py
CHANGED
@@ -14,8 +14,8 @@ asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base",
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# load text-to-speech checkpoint and speaker embeddings
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#processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
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#Use own TTS Model
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processor = SpeechT5Processor.from_pretrained("jasonl1/speecht5_finetuned_voxpopuli_fi")
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#processor = SpeechT5Processor.from_pretrained("Salama1429/TTS_German_Speecht5_finetuned_voxpopuli_nl")
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# Load model directly
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@@ -24,8 +24,8 @@ processor = SpeechT5Processor.from_pretrained("jasonl1/speecht5_finetuned_voxpop
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#model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
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#Use own TTS Model
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model = SpeechT5ForTextToSpeech.from_pretrained("jasonl1/speecht5_finetuned_voxpopuli_fi",ignore_mismatched_sizes=True,)
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vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
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@@ -42,7 +42,7 @@ speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze
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# At Inference. it should use translate(sample["audio"].copy())
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def translate(audio):
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outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "
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return outputs["text"]
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# load text-to-speech checkpoint and speaker embeddings
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#processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
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#Use own TTS Model
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#processor = SpeechT5Processor.from_pretrained("jasonl1/speecht5_finetuned_voxpopuli_fi")
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processor = SpeechT5Processor.from_pretrained("sanchit-gandhi/speecht5_tts_vox_nl")
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#processor = SpeechT5Processor.from_pretrained("Salama1429/TTS_German_Speecht5_finetuned_voxpopuli_nl")
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# Load model directly
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#model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
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#Use own TTS Model
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#model = SpeechT5ForTextToSpeech.from_pretrained("jasonl1/speecht5_finetuned_voxpopuli_fi",ignore_mismatched_sizes=True,)
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model = SpeechT5ForTextToSpeech.from_pretrained("sanchit-gandhi/speecht5_tts_vox_nl")
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vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
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# At Inference. it should use translate(sample["audio"].copy())
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def translate(audio):
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outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "nl"})
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return outputs["text"]
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