initial push

app.py

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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", device=0)
+wav2vec_fr_model = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-xlsr-53-french", device=0)
+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)

requirements.txt

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+transformers
+git+https://github.com/openai/whisper.git