sabelo /
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import os
os.system("pip install 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
return [["Invalid input"]*5]
audio = whisper.load_audio(audio)
audio = whisper.pad_or_trim(audio)
for model_name in models_selected:
start =
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]
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(( - 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]( FYI: The larger models take a lot longer to transcribe the text :)")
gr.Markdown("Here are sample audio files to try out: [Sample Audio](")
gr.Markdown("Built by:[@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), inputs=[audio_record, audio_upload, task, models_selected, language_toggle, language_selected, without_timestamps], outputs=output)