Upload app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+import numpy as np
+from pyannote.audio import Pipeline
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+# Check and set device
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {device}")
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+# Model and pipeline setup
+model_id = "distil-whisper/distil-small.en"
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+)
+model.to(device)
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    max_new_tokens=128,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+# diarization pipeline (renamed to avoid conflict)
+diarization_pipeline = Pipeline.from_pretrained(
+    "pyannote/speaker-diarization-3.0", use_auth_token=os.getenv("HF_KEY")
+)
+
+
+def transcribe(audio):
+    sr, data = audio
+    processed_data = np.array(data).astype(np.float32) / 32767.0
+    waveform_tensor = torch.tensor(processed_data[np.newaxis, :])
+
+    # results from the pipeline
+    transcription_res = pipe({"sampling_rate": sr, "raw": processed_data})["text"]
+    diarization_res = diarization_pipeline(
+        {"waveform": waveform_tensor, "sample_rate": sr}
+    )
+
+    return transcription_res, diarization_res
+
+
+demo = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(sources=["upload", "microphone"]),
+    outputs=[
+        gr.Textbox(lines=3, info="audio transcription"),
+        gr.Textbox(info="speaker diarization"),
+    ],
+    title="Automatic Speech Recognition 🗣️",
+    description="Transcribe your speech to text with distilled whisper",
+)
+
+if __name__ == "__main__":
+    demo.launch()