| import gradio as gr | |
| from transformers import pipeline | |
| import torch | |
| import librosa | |
| import soundfile | |
| SAMPLE_RATE = 16000 | |
| pipe = pipeline(model="openai/whisper-small") | |
| def transcribe(Microphone, File_Upload): | |
| warn_output = "" | |
| if (Microphone is not None) and (File_Upload is not None): | |
| warn_output = "WARNING: You've uploaded an audio file and used the microphone. " \ | |
| "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" | |
| file = Microphone | |
| elif (Microphone is None) and (File_Upload is None): | |
| return "ERROR: You have to either use the microphone or upload an audio file" | |
| elif Microphone is not None: | |
| file = Microphone | |
| else: | |
| file = File_Upload | |
| text = pipe(file)["text"] | |
| return warn_output + text | |
| iface = gr.Interface( | |
| fn=transcribe, | |
| inputs=[ | |
| gr.inputs.Audio(source="microphone", type='filepath', optional=True), | |
| gr.inputs.Audio(source="upload", type='filepath', optional=True), | |
| ], | |
| outputs="text", | |
| layout="horizontal", | |
| theme="huggingface", | |
| title="Whisper Small", | |
| description="Demo for multilingual speech recognition using the official OpenAI [Whisper small checkpoint](https://huggingface.co/openai/whisper-small).", | |
| allow_flagging='never', | |
| ) | |
| iface.launch(enable_queue=True) |