| import os |
| import gradio as gr |
| import whisper |
| from whisper import tokenizer |
| import time |
|
|
| current_size = 'base' |
| model = whisper.load_model(current_size) |
| AUTO_DETECT_LANG = "Auto Detect" |
|
|
| def transcribe(audio, state={}, model_size='tiny', delay=1.2, lang=None, translate=False): |
| time.sleep(delay - 1) |
|
|
| global current_size |
| global model |
| if model_size != current_size: |
| current_size = model_size |
| model = whisper.load_model(current_size) |
| |
| transcription = model.transcribe( |
| audio, |
| language = lang if lang != AUTO_DETECT_LANG else None |
| ) |
| state['transcription'] += transcription['text'] + " " |
|
|
| if translate: |
| x = whisper.load_audio(audio) |
| x = whisper.pad_or_trim(x) |
| mel = whisper.log_mel_spectrogram(x).to(model.device) |
|
|
| options = whisper.DecodingOptions(task = "translation") |
| translation = whisper.decode(model, mel, options) |
|
|
| state['translation'] += translation.text + " " |
|
|
| return state['transcription'], state['translation'], state, f"detected language: {transcription['language']}" |
| |
|
|
| title = "OpenAI's Whisper Real-time Demo" |
| description = "A simple demo of OpenAI's [**Whisper**](https://github.com/openai/whisper) speech recognition model. This demo runs on a CPU. For faster inference choose 'tiny' model size and set the language explicitly." |
|
|
| model_size = gr.Dropdown(label="Model size", choices=['base', 'tiny', 'small', 'medium', 'large'], value='base') |
|
|
| delay_slider = gr.inputs.Slider(minimum=1, maximum=5, default=1.2, label="Rate of transcription") |
|
|
| available_languages = sorted(tokenizer.TO_LANGUAGE_CODE.keys()) |
| available_languages = [lang.capitalize() for lang in available_languages] |
| available_languages = [AUTO_DETECT_LANG]+available_languages |
|
|
| lang_dropdown = gr.inputs.Dropdown(choices=available_languages, label="Language", default=AUTO_DETECT_LANG, type="value") |
|
|
| if lang_dropdown==AUTO_DETECT_LANG: |
| lang_dropdown=None |
|
|
| translate_checkbox = gr.inputs.Checkbox(label="Translate to English", default=False) |
|
|
|
|
|
|
| transcription_tb = gr.Textbox(label="Transcription", lines=10, max_lines=20) |
| translation_tb = gr.Textbox(label="Translation", lines=10, max_lines=20) |
| detected_lang = gr.outputs.HTML(label="Detected Language") |
|
|
| state = gr.State({"transcription": "", "translation": ""}) |
|
|
| gr.Interface( |
| fn=transcribe, |
| inputs=[ |
| gr.Audio(source="microphone", type="filepath", streaming=True), |
| state, |
| model_size, |
| delay_slider, |
| lang_dropdown, |
| translate_checkbox |
| ], |
| outputs=[ |
| transcription_tb, |
| translation_tb, |
| state, |
| detected_lang |
| ], |
| live=True, |
| allow_flagging='never', |
| title=title, |
| description=description, |
| ).launch( |
| |
| |
| ) |
