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import os |
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os.system("pip install git+https://github.com/openai/whisper.git") |
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import gradio as gr |
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import whisper |
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import io |
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import os |
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import numpy as np |
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from datetime import datetime |
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import assets |
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def sendToWhisper(audio_record, audio_upload, task, models_selected, language_toggle, language_selected, without_timestamps): |
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results = [] |
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audio = None |
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if audio_record is not None: |
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audio = audio_record |
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elif audio_upload is not None: |
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audio = audio_upload |
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else: |
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return [["Invalid input"]*5] |
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audio = whisper.load_audio(audio) |
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audio = whisper.pad_or_trim(audio) |
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for model_name in models_selected: |
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start = datetime.now() |
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model = whisper.load_model(model_name) |
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mel = whisper.log_mel_spectrogram(audio).to(model.device) |
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options = whisper.DecodingOptions(fp16 = False, without_timestamps=without_timestamps, task=task) |
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if language_toggle: |
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options = whisper.DecodingOptions(fp16 = False, without_timestamps=without_timestamps, task=task, language=language_selected) |
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language = "" |
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prob = 0 |
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if model_name in assets.lang_detect: |
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_, probs = model.detect_language(mel) |
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language = max(probs, key=probs.get) |
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prob = probs[language] |
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else: |
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language="en" |
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options = whisper.DecodingOptions(fp16 = False, without_timestamps=without_timestamps, task=task, language="en") |
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output_text = whisper.decode(model, mel, options) |
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results.append([model_name, output_text.text, language, str(prob), str((datetime.now() - start).total_seconds())]) |
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return results |
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avail_models = whisper.available_models() |
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with gr.Blocks(css=assets.css) as demo: |
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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 :)") |
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gr.Markdown("Here are sample audio files to try out: [Sample Audio](https://drive.google.com/drive/folders/1qYek06ZVeKr9f5Jf35eqi-9CnjNIp98u?usp=sharing)") |
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gr.Markdown("Built by:[@davidtsong](https://twitter.com/davidtsong)") |
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with gr.Column(): |
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gr.Markdown("## Input") |
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with gr.Row(): |
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audio_record = gr.Audio(source="microphone", label="Audio to transcribe", type="filepath",elem_id="audio_inputs") |
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audio_upload = gr.Audio(source="upload", type="filepath", interactive=True,elem_id="audio_inputs") |
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models_selected = gr.CheckboxGroup(avail_models, label="Models to use") |
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with gr.Accordion("Settings", open=False): |
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task = gr.Dropdown(["transcribe", "translate"], label="Task", value="transcribe") |
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language_toggle = gr.Dropdown(["Automatic", "Manual"], label="Language Selection", value="Automatic") |
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language_selected = gr.Dropdown(list(assets.LANGUAGES.keys()), label="Language") |
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without_timestamps = gr.Checkbox(label="Without timestamps",value=True) |
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submit = gr.Button(label="Run") |
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gr.Markdown("## Output") |
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output = gr.Dataframe(headers=["Model", "Text", "Language", "Language Confidence","Time(s)"], label="Results", wrap=True) |
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submit.click(fn=sendToWhisper, inputs=[audio_record, audio_upload, task, models_selected, language_toggle, language_selected, without_timestamps], outputs=output) |
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demo.launch() |
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