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| from transformers import pipeline | |
| asr = pipeline("automatic-speech-recognition", model="distil-whisper/distil-small.en") #sound to text model | |
| demo = gr.Blocks() | |
| def transcribe_long_form(filepath): | |
| if filepath is None: | |
| gr.Warning("No audio found, please retry.") | |
| return "" | |
| audio, sampling_rate = sf.read(filepath) #reading the converted .wav | |
| #converting audio into one dimension (stereo audio has 2, audio and spacial audio. We dont need spacial) | |
| audio_transposed = np.transpose(audio) | |
| audio_mono = librosa.to_mono(audio_transposed) | |
| IPythonAudio(audio_mono, rate=sampling_rate) | |
| #converting to same sampling rate as model | |
| audio_16KHz = librosa.resample(audio_mono, | |
| orig_sr=sampling_rate, | |
| target_sr=16000) | |
| output = asr( | |
| audio_16KHz, | |
| max_new_tokens=256, | |
| chunk_length_s=30, | |
| batch_size=12, | |
| ) | |
| return output["text"] | |
| mic_transcribe = gr.Interface( | |
| fn=transcribe_long_form, | |
| inputs=gr.Audio(sources="microphone", | |
| type="filepath"), | |
| outputs=gr.Textbox(label="Transcription", | |
| lines=3), | |
| allow_flagging="never") | |
| file_transcribe = gr.Interface( | |
| fn=transcribe_long_form, | |
| inputs=gr.Audio(sources="upload", | |
| type="filepath"), | |
| outputs=gr.Textbox(label="Transcription", | |
| lines=3), | |
| allow_flagging="never", | |
| ) | |
| with demo: | |
| gr.TabbedInterface( | |
| [mic_transcribe, | |
| file_transcribe], | |
| ["Transcribe Microphone", | |
| "Transcribe Audio File"], | |
| ) | |
| demo.launch() |