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import torch
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import gradio as gr
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from transformers import pipeline
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from transformers.pipelines.audio_utils import ffmpeg_read
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import tempfile
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import os
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MODEL_NAME = "dmatekenya/whisper-large-v3-chichewa"
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BATCH_SIZE = 8
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FILE_LIMIT_MB = 1000
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YT_LENGTH_LIMIT_S = 3600
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("openai/whisper-large-v3")
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device = 0 if torch.cuda.is_available() else "cpu"
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pipe = pipeline(
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task="automatic-speech-recognition",
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tokenizer=tokenizer,
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model=MODEL_NAME,
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chunk_length_s=30,
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device=device,
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)
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def transcribe(inputs, task):
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if inputs is None:
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raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
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text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
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return text
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demo = gr.Blocks()
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file_transcribe = gr.Interface(
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fn=transcribe,
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inputs=[
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gr.inputs.Audio(source="upload", type="filepath", optional=True, label="Audio file"),
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gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
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],
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outputs="text",
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layout="horizontal",
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theme="huggingface",
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title="Whisper Large V3: Transcribe Audio",
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description=(
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"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the OpenAI Whisper"
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f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
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" of arbitrary length."
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),
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allow_flagging="never",
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)
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with demo:
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gr.TabbedInterface([file_transcribe], [ "Audio file"])
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demo.launch(enable_queue=True)
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