Upload 3 files

app.py

@@ -1,60 +1,127 @@
-import gradio as gr
 import torch
-import whisper
+
+import gradio as gr
+import pytube as pt
+from transformers import pipeline
 from diffusers import DiffusionPipeline
-from transformers import (
-    WhisperForConditionalGeneration,
-    WhisperProcessor,
-)
 
-import os
-MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model = WhisperForConditionalGeneration.from_pretrained("whispy/whisper_italian").to(device)
-processor = WhisperProcessor.from_pretrained("whispy/whisper_italian")
+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,
+)
 
 diffuser_pipeline = DiffusionPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4",
-    custom_pipeline="speech_to_image_diffusion",
-    speech_model=model,
-    speech_processor=processor,
-    use_auth_token=MY_SECRET_TOKEN,
-    revision="fp16",
-    torch_dtype=torch.float16,
+    #custom_pipeline="speech_to_image_diffusion",
+    #speech_model=model,
+    #speech_processor=processor,
+    #use_auth_token=MY_SECRET_TOKEN,
+    #revision="fp16",
+    #torch_dtype=torch.float16,
 )
 
-diffuser_pipeline.enable_attention_slicing()
-diffuser_pipeline = diffuser_pipeline.to(device)
-
-
-#————————————————————————————————————————————
-# GRADIO SETUP
-title = "Speech to Diffusion • Community Pipeline"
-description = """
-<p style='text-align: center;'>This demo can generate an image from an audio sample using pre-trained OpenAI whisper-small and Stable Diffusion.<br />
-Community examples consist of both inference and training examples that have been added by the community.<br />
-<a href='https://github.com/huggingface/diffusers/tree/main/examples/community#speech-to-image' target='_blank'> Click here for more information about community pipelines </a>
-</p>
-"""
-article = """
-<p style='text-align: center;'>Community pipeline by Mikail Duzenli • Gradio demo by Sylvain Filoni & Ahsen Khaliq<p>
-"""
-audio_input = gr.Audio(source="microphone", type="filepath")
-image_output = gr.Image()
-
-def speech_to_text(audio_sample):
-  
-  process_audio = whisper.load_audio(audio_sample)
-  output = diffuser_pipeline(process_audio)
- 
-  print(f"""
-  ————————
-  output: {output}
-  ————————
-  """)
-  
-  return output.images[0]
-
-demo = gr.Interface(fn=speech_to_text, inputs=audio_input, outputs=image_output, title=title, description=description, article=article)
-demo.launch()
+#diffuser_pipeline.enable_attention_slicing()
+#diffuser_pipeline = diffuser_pipeline.to(device)
+
+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 = pipe(file)["text"]
+
+    translate = translator(text)
+    translate = translate[0]["translation_text"]
+
+    output = diffuser_pipeline(translate)
+    image = output.images[0]
+
+    return warn_output + text, translate, image
+
+
+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"]
+      
+    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", "image"],
+    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", "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)

requirements.txt

@@ -1,7 +1,5 @@
---extra-index-url https://download.pytorch.org/whl/cu113
+transformers
 torch
-scipy
-ftfy
-git+https://github.com/huggingface/transformers
-git+https://github.com/huggingface/diffusers
-git+https://github.com/openai/whisper.git
+pytube
+diffusers
+sentencepiece