Initial commit

.gitignore

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+venv/

gradio_app.py

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+from transformers import pipeline
+import gradio as gr
+from huggingface_hub import HfFolder
+import requests
+import asyncio
+from gtts import gTTS
+
+model_id = "sanchit-gandhi/whisper-small-dv"  # update with your model id
+pipe = pipeline("automatic-speech-recognition", model=model_id)
+
+async def query(text, model_id="tiiuae/falcon-7b-instruct"):
+    api_url = f"https://api-inference.huggingface.co/models/{model_id}"
+    headers = {"Authorization": "Bearer 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"}
+    payload = {"inputs": text}
+
+    print(f"Querying...: {text}")
+    loop = asyncio.get_event_loop()
+    response = await loop.run_in_executor(None, lambda: requests.post(api_url, headers=headers, json=payload))
+    print("\n")
+    print("\n")
+    print(response.json())
+    print("\n")
+    return response.json()[0]["generated_text"].split("\n")[1]
+
+async def transcribe_speech(filepath):
+    output = pipe(
+        filepath,
+        max_new_tokens=256,
+        generate_kwargs={
+            "task": "transcribe",
+            "language": "english",
+        },  # update with the language you've fine-tuned on
+        chunk_length_s=30,
+        batch_size=8,
+    )
+    return await query(output["text"])
+
+
+def final(filepath):
+    answer=asyncio.run(transcribe_speech(filepath))
+    return answer
+
+def main(filepath):
+    response=final(filepath)
+    # print(response)
+    # myobj = gTTS(text=response, lang='en', slow=False) 
+    # myobj.save(filepath)
+    # return filepath
+    return response
+
+mic_transcribe = gr.Interface(
+    fn=main,
+    inputs=gr.Audio(sources="microphone", type="filepath"),
+    outputs="text",
+)
+
+file_transcribe = gr.Interface(
+    fn=main,
+    inputs=gr.Audio(sources="upload", type="filepath"),
+    outputs="audio",
+)
+
+
+demo=gr.TabbedInterface(
+        [mic_transcribe, file_transcribe],
+        ["Transcribe Microphone", "Transcribe Audio File"],
+    )
+
+demo.launch(debug=True)