initial test run

app.py

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+import os
+import tempfile
+
+import torch
+import gradio as gr
+from transformers import pipeline
+
+from huggingface_hub import notebook_login, InferenceClient
+
+notebook_login()
+
+TEXT_MODEL_NAME = "microsoft/Phi-3-mini-4k-instruct"
+
+device = 0 if torch.cuda.is_available() else "cpu"
+
+AUDIO_MODEL_NAME = (
+ "distil-whisper/distil-large-v3"
+)
+BATCH_SIZE = 8
+
+pipe = pipeline(
+ task="automatic-speech-recognition",
+ model=AUDIO_MODEL_NAME,
+ chunk_length_s=30,
+ device=device,
+)
+
+def transcribe(audio_input):
+ """Function to convert audio to text."""
+ if audio_input is None:
+ raise gr.Error("No audio file submitted.")
+
+ output = pipe(audio_input, batch_size=BATCH_SIZE,
+ generate_kwargs={"task": "transcribe"},
+ return_timestamps=True)
+ return output["text"]
+
+client = InferenceClient()
+
+def build_messages(meeting_transcript) -> list:
+ system_input = "You are an assitant that organizes meeting minutes."
+ user_input = """Take this raw meeting transcript and return an organized version.
+ Here is the transcript:
+ {meeting_transcript}
+ """.format(
+ meeting_transcript=meeting_transcript
+ )
+
+ messages = [
+ {"role": "system", "content": system_input},
+ {"role": "user", "content": user_input},
+ ]
+ return messages
+
+def organize_text(meeting_transcript):
+ messages = build_messages(meeting_transcript)
+ response = client.chat_completion(messages, model=TEXT_MODEL_NAME, max_tokens=250, seed=430)
+ return response.choices[0].message.content
+
+def meeting_transcript_tool(audio_input):
+ meeting_text = transcribe(audio_input)
+ organized_text = organize_text(meeting_text)
+ return organized_text
+
+demo = gr.Interface(
+ fn=meeting_transcript_tool,
+ inputs=gr.Audio(type="filepath"),
+ outputs=gr.Textbox(show_copy_button=True),
+ title="The Complete Meeting Transcription tool",
+)
+
+demo.launch()

requirements.txt

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+torch
+transformers