whisper-v3-cpu / app.py
suisuyy
complete
7233e3e
import torch
import time
import gradio as gr
import spaces
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
DEFAULT_MODEL_NAME = "openai/whisper-tiny"
BATCH_SIZE = 8
device = 0 if torch.cuda.is_available() else "cpu"
def load_pipeline(model_name):
return pipeline(
task="automatic-speech-recognition",
model=model_name,
chunk_length_s=30,
device=device,
)
pipe = load_pipeline(DEFAULT_MODEL_NAME)
@spaces.GPU
def transcribe(inputs, task, model_name):
if inputs is None:
raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
global pipe
if model_name != pipe.model.name_or_path:
pipe = load_pipeline(model_name)
start_time = time.time() # Record the start time
text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
end_time = time.time() # Record the end time
transcription_time = end_time - start_time # Calculate the transcription time
# Create the transcription time output with additional information
transcription_time_output = (
f"Transcription Time: {transcription_time:.2f} seconds\n"
f"Model Used: {model_name}\n"
f"Device Used: {'GPU' if torch.cuda.is_available() else 'CPU'}"
)
return text, transcription_time_output
demo = gr.Blocks()
mf_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(type="filepath"),
gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
gr.Textbox(
label="Model Name",
value=DEFAULT_MODEL_NAME,
placeholder="Enter the model name",
info="Some available models: distil-whisper/distil-large-v3 distil-whisper/distil-medium.en Systran/faster-distil-whisper-large-v3 Systran/faster-whisper-large-v3 Systran/faster-whisper-medium openai/whisper-tiny , openai/whisper-base, openai/whisper-medium, openai/whisper-large-v3"
),
],
outputs=[gr.TextArea(label="Transcription"), gr.TextArea(label="Transcription Info")],
theme="huggingface",
title="Whisper Transcription",
description=(
"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the specified OpenAI Whisper"
" checkpoint and 🤗 Transformers to transcribe audio files of arbitrary length."
),
allow_flagging="never",
)
file_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(type="filepath", label="Audio file"),
gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
gr.Textbox(
label="Model Name",
value=DEFAULT_MODEL_NAME,
placeholder="Enter the model name",
info="Some available models: openai/whisper-tiny, openai/whisper-base, openai/whisper-medium, openai/whisper-large-v2"
),
],
outputs=[gr.TextArea(label="Transcription"), gr.TextArea(label="Transcription Info")],
theme="huggingface",
title="Whisper Transcription",
description=(
"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the specified OpenAI Whisper"
" checkpoint and 🤗 Transformers to transcribe audio files of arbitrary length."
),
allow_flagging="never",
)
with demo:
gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"])
demo.launch(share=True)