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, task, models_selected, language_toggle, language_selected, 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) for model_name in models_selected: 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) if language_toggle: options = whisper.DecodingOptions(fp16 = False, without_timestamps=without_timestamps, task=task, language=language_selected) language = "" prob = 0 if model_name in assets.lang_detect: _, probs = model.detect_language(mel) language = max(probs, key=probs.get) prob = probs[language] else: language="en" options = whisper.DecodingOptions(fp16 = False, without_timestamps=without_timestamps, task=task, language="en") 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, task, models_selected, language_toggle, language_selected, without_timestamps], outputs=output) demo.launch()