app load whisper

app.py

@@ -1,22 +1,101 @@
-# import high-level pipeline 
-#from transformers import pipeline
+import torch
 
-# This line will load your desired model
-#pipe = pipeline("automatic-speech-recognition", model="thennal/whisper-medium-ml")
-
-from transformers import pipeline
 import gradio as gr
+import pytube as pt
+from transformers import pipeline
+
+
+MODEL_NAME = "whispy/whisper_italian"
+
+device = 0 if torch.cuda.is_available() else "cpu"
+
+summarizer = pipeline(
+    "summarization",
+    model="it5/it5-efficient-small-el32-news-summarization",
+)
+
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=MODEL_NAME,
+    chunk_length_s=30,
+    device=device,
+)
+
+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
 
-modelo = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-xlsr-53-spanish")
+    text = pipe(file)["text"]
 
-def transcribe(audio):
-  text = modelo(audio)["text"]
-  return text
+    return warn_output + text
 
 
-gr.Interface(
+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 = pipe("audio.mp3")["text"]
+    summary = summarizer(text)
+    summary = summary[0]["summary_text"]
+
+    return html_embed_str, text, summary
+
+demo = gr.Blocks()
+
+mf_transcribe = gr.Interface(
     fn=transcribe,
-    inputs=gr.File(files_type=['.mp3'], label="Upload audio file"),
-    outputs="textbox"
-).launch()
+    inputs=[
+        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
+        gr.inputs.Audio(source="upload", type="filepath", optional=True),
+    ],
+    outputs="text",
+    layout="horizontal",
+    theme="huggingface",
+    title="Whisper Demo: Transcribe Audio",
+    description=(
+        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the the fine-tuned"
+        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
+        " of arbitrary length."
+    ),
+    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"],
+    layout="horizontal",
+    theme="huggingface",
+    title="Whisper Demo: Transcribe YouTube",
+    description=(
+        "Transcribe long-form YouTube videos with the click of a button! Demo uses the the fine-tuned checkpoint:"
+        f" [{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, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"])
+
+demo.launch(enable_queue=True)