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artyomboyko
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c1990e6
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c9c650d
Update app.py
Browse filesAdd the code debugged on the local machine
app.py
CHANGED
@@ -5,13 +5,16 @@ from datasets import load_dataset
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from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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#
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asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
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#
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processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
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model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
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@@ -22,11 +25,15 @@ speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze
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def translate(audio):
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def synthesise(text):
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inputs = processor(text=text, return_tensors="pt")
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speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
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return speech.cpu()
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@@ -43,7 +50,6 @@ 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 English. 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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"""
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@@ -69,4 +75,4 @@ file_translate = gr.Interface(
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with demo:
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gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])
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demo.launch()
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from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline
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from transliterate import translit
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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# загрузить контрольную точку модели транскибации и перевода речи
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asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
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translate_pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ru")
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# Загрузим контрольную точку преобразования текста в речь и эбеддинги дикторов
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processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
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model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
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def translate(audio):
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transcription = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "english"})
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translation = translate_pipe(transcription["text"])
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result = translit(translation[0]['translation_text'], "ru", reversed=True)
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print(result)
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return result
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def synthesise(text):
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print(text)
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inputs = processor(text=text, return_tensors="pt")
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speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
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return speech.cpu()
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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 English. 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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"""
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with demo:
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gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])
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demo.launch()
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