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app.py
CHANGED
@@ -12,9 +12,9 @@ device = "cuda:0" if torch.cuda.is_available() else "cpu"
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asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
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# load text-to-speech checkpoint and speaker embeddings
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processor = SpeechT5Processor.from_pretrained("
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model = SpeechT5ForTextToSpeech.from_pretrained("
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vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
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embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
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@@ -22,7 +22,7 @@ speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze
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def translate(audio):
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outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "
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return outputs["text"]
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@@ -41,7 +41,7 @@ def speech_to_speech_translation(audio):
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title = "Cascaded STST"
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description = """
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Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in
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[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model for text-to-speech:
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![Cascaded STST](https://huggingface.co/datasets/huggingface-course/audio-course-images/resolve/main/s2st_cascaded.png "Diagram of cascaded speech to speech translation")
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asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
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# load text-to-speech checkpoint and speaker embeddings
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processor = SpeechT5Processor.from_pretrained("arpan-das-astrophysics/speecht5_finetuned_voxpopuli_it")
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model = SpeechT5ForTextToSpeech.from_pretrained("arpan-das-astrophysics/speecht5_finetuned_voxpopuli_it").to(device)
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vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
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embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
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def translate(audio):
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outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "it"})
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return outputs["text"]
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title = "Cascaded STST"
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description = """
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Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in Italian. Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech translation, and Microsoft's
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[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model for text-to-speech:
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![Cascaded STST](https://huggingface.co/datasets/huggingface-course/audio-course-images/resolve/main/s2st_cascaded.png "Diagram of cascaded speech to speech translation")
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