upload app files

app.py

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+import torch
+
+import gradio as gr
+import yt_dlp as youtube_dl
+from transformers import pipeline
+from transformers.pipelines.audio_utils import ffmpeg_read
+from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
+
+
+import tempfile
+import os
+
+MODEL_NAME = "openai/whisper-medium"
+BATCH_SIZE = 8
+FILE_LIMIT_MB = 1000
+
+device = 0 if torch.cuda.is_available() else "cpu"
+
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=MODEL_NAME,
+    chunk_length_s=30,
+    device=device,
+)
+
+model = MBartForConditionalGeneration.from_pretrained("sanjitaa/mbart-many-to-many")
+tokenizer = MBart50TokenizerFast.from_pretrained("sanjitaa/mbart-many-to-many")
+
+def translate(inputs, task):
+    if inputs is None:
+        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
+
+    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
+
+    encoded_text = tokenizer(text, return_tensors="pt")
+    tokenizer.src_lang = "en_XX"
+    
+    generated_tokens = model.generate(
+        **encoded_text,
+        forced_bos_token_id=tokenizer.lang_code_to_id["fr_XX"]
+    )
+    result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+    return result
+        
+
+demo = gr.Blocks()
+
+mf_transcribe = gr.Interface(
+    fn=translate,
+    inputs=[
+        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
+        gr.inputs.Radio(["translate"], label="Task", default="translate"),
+        
+    ],
+    outputs="text",
+    layout="horizontal",
+    theme="huggingface",
+    title="Whisper Medium: Transcribe Audio",
+    description=(
+        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
+        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
+        " of arbitrary length."
+    ),
+    allow_flagging="never",
+)
+
+file_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.inputs.Audio(source="upload", type="filepath", optional=True, label="Audio file"),
+        gr.inputs.Radio(["translate"], label="Task", default="transcribe"),
+    ],
+    outputs="text",
+    layout="horizontal",
+    theme="huggingface",
+    title="Whisper Large V2: Transcribe Audio",
+    description=(
+        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
+        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) 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(enable_queue=True)
+

packages.txt

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+ffmpeg

requirements.txt

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+git+https://github.com/huggingface/transformers
+torch
+yt-dlp