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| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from datasets import load_dataset | |
| import librosa | |
| from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline | |
| from transformers import WhisperProcessor, WhisperForConditionalGeneration | |
| 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) | |
| asr_processor = WhisperProcessor.from_pretrained("openai/whisper-base") | |
| asr_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base").to(device) | |
| asr_forced_decoder_ids = asr_processor.get_decoder_prompt_ids(language="dutch", task="transcribe") | |
| # load text-to-speech checkpoint and speaker embeddings | |
| if 0: | |
| processor = SpeechT5Processor.from_pretrained("sanchit-gandhi/speecht5_tts_vox_nl") | |
| model = SpeechT5ForTextToSpeech.from_pretrained("sanchit-gandhi/speecht5_tts_vox_nl").to(device) | |
| if 1: | |
| from transformers import VitsModel, VitsTokenizer | |
| model = VitsModel.from_pretrained("Matthijs/mms-tts-fra") | |
| tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-fra") | |
| vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device) | |
| embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") | |
| speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) | |
| def translate(audio): | |
| if 0: | |
| outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"language":"dutch", "task":"transcribe"}) | |
| return outputs["text"] | |
| else: | |
| x, sr = librosa.load(audio) | |
| input_features = asr_processor(x, sampling_rate=16000, return_tensors="pt").input_features | |
| predicted_ids = asr_model.generate(input_features, forced_decoder_ids=asr_forced_decoder_ids) | |
| # decode token ids to text | |
| transcription = asr_processor.batch_decode(predicted_ids, skip_special_tokens=True) | |
| return transcription | |
| def synthesise(text): | |
| if 0: | |
| 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() | |
| if 1: | |
| inputs = tokenizer(text, return_tensors="pt") | |
| input_ids = inputs["input_ids"] | |
| with torch.no_grad(): | |
| outputs = model(input_ids) | |
| speech = outputs.audio[0] | |
| return speech.cpu() | |
| def speech_to_speech_translation(audio): | |
| translated_text = translate(audio) | |
| print(translated_text) | |
| 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 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() |