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| import torch | |
| import os | |
| from transformers import pipeline, VitsModel, VitsTokenizer, SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor | |
| import numpy as np | |
| os.system("pip install git+https://github.com/openai/whisper.git") | |
| import gradio as gr | |
| import whisper | |
| model = whisper.load_model("small") | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| def inference(audio): | |
| audio = whisper.load_audio(audio) | |
| print("loading finished") | |
| audio = whisper.pad_or_trim(audio) | |
| print("audio trimed") | |
| mel = whisper.log_mel_spectrogram(audio).to(model.device) | |
| print("spectro finished") | |
| _, probs = model.detect_language(mel) | |
| print("lang detected") | |
| options = whisper.DecodingOptions(fp16 = False) | |
| print("options decoded") | |
| result = whisper.decode(model, mel, options) | |
| print(result.text) | |
| return result.text | |
| # 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-eng") | |
| #tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-eng") | |
| model2 = VitsModel.from_pretrained("facebook/mms-tts-eng") | |
| tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-eng") | |
| # Define a function to translate an audio, in english here | |
| def translate(audio): | |
| # return inference(audio) | |
| outputs = pipe(audio, max_new_tokens=256, | |
| generate_kwargs={"task": "translate"}) | |
| 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 = model2(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 [translated_text, (16000, synthesised_speech)] | |
| def predict(transType, language, audio, audio_mic = None): | |
| print("debug1:", audio,"debug2", audio_mic) | |
| if not audio and audio_mic: | |
| audio = audio_mic | |
| if transType == "Text": | |
| return translate(audio), None | |
| 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.wav", None], | |
| # ["Audio", "Swedish", "./ex2.wav", 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="filepath"), | |
| #gr.Audio(label="Import an audio", sources="upload", type="numpy"), | |
| gr.Audio(label="Record an audio", sources="microphone", type="filepath"), | |
| ], | |
| outputs=[ | |
| gr.Text(label="Text translation"),gr.Audio(label="Audio translation",type = "numpy") | |
| ], | |
| title=title, | |
| description=description, | |
| article="", | |
| examples=examples, | |
| ) | |
| demo.launch() |