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| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from transformers import BarkModel | |
| from transformers import AutoProcessor | |
| from transformers import pipeline | |
| import librosa | |
| processor = AutoProcessor.from_pretrained("suno/bark-small") | |
| model = BarkModel.from_pretrained("suno/bark-small") | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| model = model.to(device) | |
| # https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c | |
| language_presets = {"es":"v2/es_speaker_", | |
| "en":"v2/en_speaker_"} | |
| def tts(text, language="es", style:int = 0): | |
| voice_preset = language_presets[language] + str(style) | |
| # prepare the inputs | |
| inputs = processor(text, voice_preset = voice_preset) | |
| # generate speech | |
| speech_output = model.generate(**inputs.to(device)) | |
| sampling_rate = model.generation_config.sample_rate | |
| return speech_output[0].cpu().numpy(), sampling_rate | |
| # load speech translation checkpoint | |
| asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device) | |
| def translate(audio, language:str = "es"): | |
| outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language":language}) | |
| text = outputs["text"] | |
| return text | |
| def synthesise(text, language="es",style=0): | |
| speech, sr = tts(text, language=language, style=style) | |
| target_sr = 16_000 | |
| speech = librosa.resample(speech, orig_sr = sr, target_sr = target_sr) | |
| return speech, target_sr | |
| def speech_to_speech_translation(audio, debug = True): | |
| translated_text = translate(audio) | |
| if debug: | |
| print(f"{translated_text=}") | |
| synthesised_speech, sampling_rate = synthesise(translated_text) | |
| # tranform to int for Gradio | |
| synthesised_speech = (np.array(synthesised_speech) * 32767).astype(np.int16) | |
| return sampling_rate, synthesised_speech | |
| title = "Cascaded STST" | |
| description = """ | |
| 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 | |
| [SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model for text-to-speech: | |
|  | |
| """ | |
| demo = gr.Blocks() | |
| mic_translate = gr.Interface( | |
| fn=speech_to_speech_translation, | |
| inputs=gr.Audio(source="microphone", type="filepath"), | |
| outputs=gr.Audio(label="Generated Speech", type="numpy"), | |
| title=title, | |
| description=description, | |
| ) | |
| file_translate = gr.Interface( | |
| fn=speech_to_speech_translation, | |
| inputs=gr.Audio(source="upload", type="filepath"), | |
| outputs=gr.Audio(label="Generated Speech", type="numpy"), | |
| examples=[["./example.wav"]], | |
| title=title, | |
| description=description, | |
| ) | |
| with demo: | |
| gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"]) | |
| demo.launch() | |