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import gradio as gr |
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import numpy as np |
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import torch |
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from transformers import ( |
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VitsModel, |
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VitsTokenizer, |
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pipeline |
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) |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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asr_pipe = pipeline( |
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"automatic-speech-recognition", |
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model="openai/whisper-base", |
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device=device |
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) |
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model = VitsModel.from_pretrained("Matthijs/mms-tts-deu") |
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tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-deu") |
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def translate(audio): |
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outputs = asr_pipe( |
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audio, |
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max_new_tokens=256, |
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generate_kwargs={"task": "transcribe", "language": "de"} |
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) |
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return outputs["text"] |
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def synthesise(text): |
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if len(text.strip()) == 0: |
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return (16000, np.zeros(0).astype(np.int16)) |
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inputs = tokenizer(text, return_tensors="pt") |
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input_ids = inputs["input_ids"] |
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with torch.no_grad(): |
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outputs = model(input_ids) |
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speech = outputs.audio[0] |
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return speech.cpu() |
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def speech_to_speech_translation(audio): |
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translated_text = translate(audio) |
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synthesised_speech = synthesise(translated_text) |
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synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16) |
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return 16000, synthesised_speech |
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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 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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 |
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""" |
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demo = gr.Blocks() |
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mic_translate = gr.Interface( |
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fn=speech_to_speech_translation, |
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inputs=gr.Audio(source="microphone", type="filepath"), |
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outputs=gr.Audio(label="Generated Speech", type="numpy"), |
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title=title, |
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description=description, |
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) |
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file_translate = gr.Interface( |
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fn=speech_to_speech_translation, |
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inputs=gr.Audio(source="upload", type="filepath"), |
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outputs=gr.Audio(label="Generated Speech", type="numpy"), |
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examples=[["./example.wav"]], |
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title=title, |
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description=description, |
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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()import gradio as gr |
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import numpy as np |
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import torch |
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from transformers import ( |
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VitsModel, |
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VitsTokenizer, |
|
pipeline |
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) |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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asr_pipe = pipeline( |
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"automatic-speech-recognition", |
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model="openai/whisper-base", |
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device=device |
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) |
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model = VitsModel.from_pretrained("Matthijs/mms-tts-deu") |
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tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-deu") |
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def translate(audio): |
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outputs = asr_pipe( |
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audio, |
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max_new_tokens=256, |
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generate_kwargs={"task": "transcribe", "language": "de"} |
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) |
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return outputs["text"] |
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def synthesise(text): |
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if len(text.strip()) == 0: |
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return (16000, np.zeros(0).astype(np.int16)) |
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inputs = tokenizer(text, return_tensors="pt") |
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input_ids = inputs["input_ids"] |
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with torch.no_grad(): |
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outputs = model(input_ids) |
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speech = outputs.audio[0] |
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return speech.cpu() |
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def speech_to_speech_translation(audio): |
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translated_text = translate(audio) |
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synthesised_speech = synthesise(translated_text) |
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synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16) |
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return 16000, synthesised_speech |
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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 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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 |
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""" |
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demo = gr.Blocks() |
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mic_translate = gr.Interface( |
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fn=speech_to_speech_translation, |
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inputs=gr.Audio(source="microphone", type="filepath"), |
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outputs=gr.Audio(label="Generated Speech", type="numpy"), |
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title=title, |
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description=description, |
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) |
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file_translate = gr.Interface( |
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fn=speech_to_speech_translation, |
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inputs=gr.Audio(source="upload", type="filepath"), |
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outputs=gr.Audio(label="Generated Speech", type="numpy"), |
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examples=[["./example.wav"]], |
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title=title, |
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description=description, |
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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() |
