| # import gradio as gr | |
| # import numpy as np | |
| # import torch | |
| # from datasets import load_dataset | |
| # from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline | |
| # device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| # # load speech translation checkpoint | |
| # asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-tiny", device=device) | |
| # # load text-to-speech checkpoint and speaker embeddings | |
| # model_id = "microsoft/speecht5_tts" #"Ellight/speecht5_finetuned_voxpopuli_nl" # update with your model id | |
| # # pipe = pipeline("automatic-speech-recognition", model=model_id) | |
| # model = SpeechT5ForTextToSpeech.from_pretrained(model_id) | |
| # vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") | |
| # embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation",trust_remote_code=True)) | |
| # speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) | |
| # # speaker_embeddings = torch.tensor(embeddings_dataset[7440]["xvector"]).unsqueeze(0) | |
| # processor = SpeechT5Processor.from_pretrained(model_id) | |
| # replacements = [ | |
| # ("à", "a"), | |
| # ("ç", "c"), | |
| # ("è", "e"), | |
| # ("ë", "e"), | |
| # ("í", "i"), | |
| # ("ï", "i"), | |
| # ("ö", "o"), | |
| # ("ü", "u"), | |
| # ] | |
| # def cleanup_text(text): | |
| # for src, dst in replacements: | |
| # text = text.replace(src, dst) | |
| # return text | |
| # def synthesize_speech(text): | |
| # text = cleanup_text(text) | |
| # inputs = processor(text=text, return_tensors="pt") | |
| # speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder) | |
| # return gr.Audio.update(value=(16000, speech.cpu().numpy())) | |
| # def translate(audio): | |
| # outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "Dutch"}) | |
| # return outputs["text"] | |
| # def synthesise(text): | |
| # text = cleanup_text(text) | |
| # inputs = processor(text=text, return_tensors="pt") | |
| # speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder) | |
| # return speech.cpu() | |
| # def speech_to_speech_translation(audio): | |
| # translated_text = translate(audio) | |
| # synthesised_speech = synthesise(translated_text) | |
| # synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16) | |
| # return 16000, synthesised_speech | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from datasets import load_dataset | |
| from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline | |
| from transformers import VitsModel, VitsTokenizer | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| # load speech translation checkpoint | |
| asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device) | |
| # load text-to-speech checkpoint and speaker embeddings | |
| # processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts") | |
| # model = SpeechT5ForTextToSpeech.from_pretrained("sanchit-gandhi/speecht5_tts_vox_nl").to(device) | |
| vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device) | |
| model = VitsModel.from_pretrained("Matthijs/mms-tts-nld") | |
| tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-nld") | |
| embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") | |
| speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) | |
| def translate(audio): | |
| outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "nl"}) | |
| return outputs["text"] | |
| def synthesise(text): | |
| inputs = tokenizer(text, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model(inputs["input_ids"]) | |
| speech = outputs.audio[0] | |
| return speech.cpu() | |
| # def synthesise(text): | |
| # inputs = processor(text=text, return_tensors="pt", padding='max_length', truncation=True) | |
| # speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder) | |
| # return speech.cpu() | |
| def speech_to_speech_translation(audio): | |
| translated_text = translate(audio) | |
| synthesised_speech = synthesise(translated_text) | |
| synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16) | |
| return 16000, 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 Dutch. Demo uses OpenAI's [Whisper Large v2](https://huggingface.co/openai/whisper-large-v2) model for speech translation, and [Sandiago21/speecht5_finetuned_voxpopuli_it](https://huggingface.co/Sandiago21/speecht5_finetuned_voxpopuli_it) checkpoint for text-to-speech, which is based on Microsoft's | |
| [SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model for text-to-speech, fine-tuned in Dutch Audio dataset: | |
|  | |
| """ | |
| 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() |