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app.py

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+import torch
+
+import gradio as gr
+import pytube as pt
+from transformers import pipeline
+
+asr = pipeline(
+    task="automatic-speech-recognition",
+    model="whispy/whisper_hf",
+    chunk_length_s=30,
+    device="cpu",
+)
+
+summarizer = pipeline(
+    "summarization",
+    model="it5/it5-efficient-small-el32-news-summarization",
+)
+
+translator = pipeline(
+    "translation", 
+    model="Helsinki-NLP/opus-mt-it-en")
+
+def transcribe(microphone, file_upload):
+    warn_output = ""
+    if (microphone is not None) and (file_upload is not None):
+        warn_output = (
+            "WARNING: You've uploaded an audio file and used the microphone. "
+            "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
+        )
+
+    elif (microphone is None) and (file_upload is None):
+        return "ERROR: You have to either use the microphone or upload an audio file"
+
+    file = microphone if microphone is not None else file_upload
+
+    text = asr(file)["text"]
+
+    translate = translator(text)
+    translate = translate[0]["translation_text"]
+
+    return warn_output + text, translate
+
+def _return_yt_html_embed(yt_url):
+    video_id = yt_url.split("?v=")[-1]
+    HTML_str = (
+        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
+        " </center>"
+    )
+    return HTML_str
+
+
+def yt_transcribe(yt_url):
+    yt = pt.YouTube(yt_url)
+    html_embed_str = _return_yt_html_embed(yt_url)
+    stream = yt.streams.filter(only_audio=True)[0]
+    stream.download(filename="audio.mp3")
+
+    text = asr("audio.mp3")["text"]
+
+    summary = summarizer(text)
+    summary = summary[0]["summary_text"]
+      
+    translate = translator(summary)
+    translate = translate[0]["translation_text"]
+
+    return html_embed_str, text, summary, translate
+
+demo = gr.Blocks()
+
+mf_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
+        gr.inputs.Audio(source="upload", type="filepath", optional=True),
+    ],
+    outputs=["text", "text"],
+    layout="horizontal",
+    theme="huggingface",
+    title="Whisper Demo: Transcribe and Translate Italian Audio",
+    description=(
+        "Transcribe and Translate long-form microphone or audio inputs with the click of a button! Demo uses the the fine-tuned"
+        f" [whispy/whisper_hf](https://huggingface.co/whispy/whisper_hf) and 🤗 Transformers to transcribe audio files"
+        " of arbitrary length. It also uses another model for the translation."
+    ),
+    allow_flagging="never",
+)
+
+yt_transcribe = gr.Interface(
+    fn=yt_transcribe,
+    inputs=[gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")],
+    outputs=["html", "text", "text", "text"],
+    layout="horizontal",
+    theme="huggingface",
+    title="Whisper Demo: Transcribe, Summarize and Translate YouTube",
+    description=(
+        "Transcribe, Summarize and Translate long-form YouTube videos with the click of a button! Demo uses the the fine-tuned "
+        f" [whispy/whisper_hf](https://huggingface.co/whispy/whisper_hf) and 🤗 Transformers to transcribe audio files of"
+        " arbitrary length. It also uses other two models to first summarize and then translate the text input. You can try with the following examples: " 
+        f" [Video1](https://www.youtube.com/watch?v=xhWhyu8cBTk)"
+        f" [Video2](https://www.youtube.com/watch?v=C6Vw_Z3t_2U)"
+    ),
+    allow_flagging="never",
+)
+
+with demo:
+    gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe and Translate Audio", "Transcribe, Summarize and Translate YouTube"])
+
+demo.launch(enable_queue=True)

packages.txt

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+ffmpeg

requirements.txt

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+transformers
+torch
+pytube
+sentencepiece