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from faster_whisper import WhisperModel
import pandas as pd
import gradio as gr
import psutil
import time
import whisperx
model = WhisperModel('large-v2', device="cpu", compute_type="int8")
def speech_to_text(mic=None, file=None, lang=None):
if mic is not None:
audio = mic
elif file is not None:
audio = file
else:
raise gr.Error("You must either provide a mic recording or a file")
if lang is None or lang == '':
lang = None
task = 'transcribe' if lang is None else 'translate'
print(lang, task)
time_start = time.time()
segments, info = model.transcribe(audio, task=task, language=lang, beam_size=5)
print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
# Decode audio to Text
objects = [s._asdict() for s in segments]
time_end = time.time()
time_diff = time_end - time_start
#memory = psutil.virtual_memory()
# *Memory: {memory.total / (1024 * 1024 * 1024):.2f}GB, used: {memory.percent}%, available: {memory.available / (1024 * 1024 * 1024):.2f}GB.*
system_info = f"""
*Processing time: {time_diff:.5} seconds.*
"""
df_results = pd.DataFrame(objects)
df_results = df_results.drop(columns=['seek', 'tokens', 'avg_logprob'])
return df_results, system_info
theme=gr.themes.Default().set(
color_accent="#e20074",
# Buttons
button_primary_text_color='white',
button_primary_text_color_hover='black',
button_primary_background_fill="#e20074",
button_primary_background_fill_hover='#c00063', # --telekom-color-primary-hovered
button_primary_border_color="#e20074",
button_primary_border_color_hover="#c00063",
stat_background_fill="#e20074",
# Dark Mode
button_primary_background_fill_dark="#e20074",
button_primary_background_fill_hover_dark='#c00063', # --telekom-color-primary-hovered
button_primary_border_color_dark="#e20074",
button_primary_border_color_hover_dark="#c00063",
stat_background_fill_dark="#e20074",
)
with gr.Blocks(title='Whisper Demo', theme=theme) as demo:
gr.Markdown('''
<div>
<h1 style='text-align: center'>Simple Whisper Demo</h1>
A simple Whisper demo using local CPU Inference of the largest-v2 Model
</div>
''')
audio_in = gr.Audio(label="Record", source='microphone', type="filepath")
file_in = gr.Audio(label="Upload", source='upload', type="filepath")
drop_down = gr.Dropdown(["de", "en", "es", "fr", "ru", None], value=None)
transcribe_btn = gr.Button("Transcribe audio", variant="primary")
translate_btn = gr.Button("Translate audio")
trans_df = gr.DataFrame(label="Transcription dataframe", row_count=(0, "dynamic"), max_rows = 10, wrap=True, overflow_row_behaviour='paginate')
sys_info = gr.Markdown("")
transcribe_btn.click(speech_to_text,
[audio_in, file_in, drop_down],
[trans_df, sys_info]
)
translate_btn.click(speech_to_text,
[audio_in, file_in, drop_down],
[trans_df, sys_info])
demo.launch()
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