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| import os | |
| os.system("pip install git+https://github.com/openai/whisper.git") | |
| import gradio as gr | |
| import whisper | |
| import io | |
| import os | |
| import numpy as np | |
| from datetime import datetime | |
| import assets | |
| def sendToWhisper(audio_record, audio_upload, without_timestamps): | |
| results = [] | |
| audio = None | |
| if audio_record is not None: | |
| audio = audio_record | |
| elif audio_upload is not None: | |
| audio = audio_upload | |
| else: | |
| return [["Invalid input"]*5] | |
| audio = whisper.load_audio(audio) | |
| audio = whisper.pad_or_trim(audio) | |
| start = datetime.now() | |
| model = whisper.load_model(model_name) | |
| mel = whisper.log_mel_spectrogram(audio).to(model.device) | |
| # options = whisper.DecodingOptions(fp16 = False, without_timestamps=without_timestamps, task=task) | |
| options = whisper.DecodingOptions() | |
| output_text = whisper.decode(model, mel, options) | |
| results.append([model_name, output_text.text, language, str(prob), str((datetime.now() - start).total_seconds())]) | |
| return results | |
| avail_models = whisper.available_models() | |
| with gr.Blocks(css=assets.css) as demo: | |
| gr.Markdown("This is a demo to use Open AI's Speech to Text (ASR) Model: Whisper. Learn more about the models here on [Github](https://github.com/openai/whisper/search?q=DecodingOptions&type=) FYI: The larger models take a lot longer to transcribe the text :)") | |
| gr.Markdown("Here are sample audio files to try out: [Sample Audio](https://drive.google.com/drive/folders/1qYek06ZVeKr9f5Jf35eqi-9CnjNIp98u?usp=sharing)") | |
| gr.Markdown("Built by:[@davidtsong](https://twitter.com/davidtsong)") | |
| # with gr.Row(): | |
| with gr.Column(): | |
| # with gr.Column(): | |
| gr.Markdown("## Input") | |
| with gr.Row(): | |
| audio_record = gr.Audio(source="microphone", label="Audio to transcribe", type="filepath",elem_id="audio_inputs") | |
| audio_upload = gr.Audio(source="upload", type="filepath", interactive=True,elem_id="audio_inputs") | |
| models_selected = gr.CheckboxGroup(avail_models, label="Models to use") | |
| with gr.Accordion("Settings", open=False): | |
| task = gr.Dropdown(["transcribe", "translate"], label="Task", value="transcribe") | |
| language_toggle = gr.Dropdown(["Automatic", "Manual"], label="Language Selection", value="Automatic") | |
| language_selected = gr.Dropdown(list(assets.LANGUAGES.keys()), label="Language") | |
| without_timestamps = gr.Checkbox(label="Without timestamps",value=True) | |
| submit = gr.Button(label="Run") | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| gr.Markdown("## Output") | |
| output = gr.Dataframe(headers=["Model", "Text", "Language", "Language Confidence","Time(s)"], label="Results", wrap=True) | |
| submit.click(fn=sendToWhisper, inputs=[audio_record, audio_upload, without_timestamps], outputs=output) | |
| # [audio_record, audio_upload, task, models_selected, language_toggle, language_selected, without_timestamps], outputs=output) | |
| demo.launch() |