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| from transformers import pipeline | |
| import gradio as gr | |
| import whisper | |
| wav2vec_en_model = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h") | |
| wav2vec_fr_model = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-xlsr-53-french") | |
| whisper_model = whisper.load_model("base") | |
| def transcribe_audio(language=None, mic=None, file=None): | |
| print(language) | |
| if mic is not None: | |
| audio = mic | |
| elif file is not None: | |
| audio = file | |
| else: | |
| return "You must either provide a mic recording or a file" | |
| wav2vec_model = load_models(language) | |
| transcription = wav2vec_model(audio)["text"] | |
| transcription2 = whisper_model.transcribe(audio, language=language)["text"] | |
| return transcription, transcription2 | |
| def load_models(lang): | |
| if lang == 'en': | |
| return wav2vec_en_model | |
| elif lang == 'fr': | |
| return wav2vec_fr_model | |
| else: | |
| # default english | |
| return wav2vec_en_model | |
| title = "Speech2text comparison (Wav2vec vs Whisper)" | |
| description = """ | |
| This Space allows easy comparisons for transcribed texts between Facebook's Wav2vec model and newly released OpenAI's Whisper model.\n | |
| (Even if Whisper includes a language detection, here we have decided to select the language to speed up the computation and to focus only on the quality of the transcriptions. The default language is english) | |
| """ | |
| article = "Check out [the OpenAI Whisper model](https://github.com/openai/whisper) and [the Facebook Wav2vec model](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) that this demo is based off of." | |
| examples = [["english_sentence.flac"], ["2022-a-Droite-un-fauteuil-pour-trois-3034044.mp3000.mp3"]] | |
| gr.Interface( | |
| fn=transcribe_audio, | |
| inputs=[ | |
| gr.Radio(label="Language", choices=["en", "fr"], value="en"), | |
| gr.Audio(source="microphone", type="filepath", optional=True), | |
| gr.Audio(source="upload", type="filepath", optional=True), | |
| ], | |
| outputs=[ | |
| gr.Textbox(label="facebook/wav2vec"), | |
| gr.Textbox(label="openai/whisper"),], | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples | |
| ).launch(debug=True) |