Spaces:
Runtime error
Runtime error
| import torch | |
| from transformers import pipeline, VitsModel, VitsTokenizer | |
| import numpy as np | |
| import gradio as gr | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| # Load Whisper-small | |
| pipe = pipeline("automatic-speech-recognition", | |
| model="openai/whisper-small", | |
| device=device | |
| ) | |
| # Load the model checkpoint and tokenizer | |
| #model = VitsModel.from_pretrained("Matthijs/mms-tts-fra") | |
| #tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-fra") | |
| model = VitsModel.from_pretrained("facebook/mms-tts-fra") | |
| tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-fra") | |
| # Define a function to translate an audio, in english here | |
| def translate(audio): | |
| outputs = pipe(audio, max_new_tokens=256, | |
| generate_kwargs={"task": "transcribe", "language": "eng"}) | |
| return outputs["text"] | |
| # Define function to generate the waveform output | |
| def synthesise(text): | |
| inputs = tokenizer(text, return_tensors="pt") | |
| input_ids = inputs["input_ids"] | |
| with torch.no_grad(): | |
| outputs = model(input_ids) | |
| return outputs.audio[0] | |
| # Define the pipeline | |
| 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 | |
| def predict(transType, language, audio, audio_mic = None): | |
| if not audio and audio_mic: | |
| audio = audio_mic | |
| if transType == "Text": | |
| return translate(audio) | |
| if transType == "Audio": | |
| return speech_to_speech_translation(audio) | |
| # Define the title etc | |
| title = "Swedish STSOT (Speech To Speech Or Text)" | |
| description="Use Whisper pretrained model to convert swedish audio to english (text or audio)" | |
| supportLangs = ["Swedish", "French (in training)"] | |
| transTypes = ["Text", "Audio"] | |
| # examples = [ | |
| # ["Text", "Swedish", "ex1.mp3", None], | |
| # ["Audio", "Swedish", "ex2.mp3", None] | |
| # ] | |
| examples = [] | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs=[ | |
| gr.Radio(label="Choose your output format", choices=transTypes), | |
| gr.Radio(label="Choose a source language", choices=supportLangs, value="Swedish"), | |
| gr.Audio(label="Import an audio", sources="upload", type="numpy"), | |
| gr.Audio(label="Record an audio", sources="microphone", type="numpy"), | |
| ], | |
| outputs=[ | |
| gr.Text(label="Translation"), | |
| ], | |
| title=title, | |
| description=description, | |
| article="", | |
| examples=examples, | |
| ).launch() | |
| demo.launch() |