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| import gradio as gr | |
| from transformers import pipeline | |
| model_names = [ | |
| "juliensimon/wav2vec2-conformer-rel-pos-large-finetuned-speech-commands", | |
| "MIT/ast-finetuned-speech-commands-v2", | |
| ] | |
| def process(file, model_name): | |
| p = pipeline("audio-classification", model=model_name) | |
| pred = p(file) | |
| return {x["label"]: x["score"] for x in pred} | |
| # Gradio inputs | |
| mic = gr.Audio(source="microphone", type="filepath", label="Speech input") | |
| model_selection = gr.Dropdown(model_names, label="Model selection") | |
| # Gradio outputs | |
| labels = gr.Label(num_top_classes=3) | |
| description = "This Space showcases two audio classification models fine-tuned on the speech_commands dataset:\n\n - wav2vec2-conformer: 97.2% accuracy, added in transformers 4.20.0.\n - audio-spectrogram-transformer: 98.12% accuracy, added in transformers 4.25.1.\n \n They can spot one of the following keywords: 'Yes', 'No', 'Up', 'Down', 'Left', 'Right', 'On', 'Off', 'Stop', 'Go', 'Zero', 'One', 'Two', 'Three', 'Four', 'Five', 'Six', 'Seven', 'Eight', 'Nine', 'Bed', 'Bird', 'Cat', 'Dog', 'Happy', 'House', 'Marvin', 'Sheila', 'Tree', 'Wow', 'Backward', 'Forward', 'Follow', 'Learn', 'Visual'." | |
| iface = gr.Interface( | |
| theme="huggingface", | |
| description=description, | |
| fn=process, | |
| inputs=[mic, model_selection], | |
| outputs=[labels], | |
| examples=[ | |
| ["backward16k.wav", "MIT/ast-finetuned-speech-commands-v2"], | |
| ["happy16k.wav", "MIT/ast-finetuned-speech-commands-v2"], | |
| ["marvin16k.wav", "MIT/ast-finetuned-speech-commands-v2"], | |
| ["seven16k.wav", "MIT/ast-finetuned-speech-commands-v2"], | |
| ["stop16k.wav", "MIT/ast-finetuned-speech-commands-v2"], | |
| ["up16k.wav", "MIT/ast-finetuned-speech-commands-v2"], | |
| ], | |
| allow_flagging="never", | |
| ) | |
| iface.launch() | |