Create app.py

app.py

@@ -0,0 +1,58 @@
+import gradio as gr
+import torch
+from wenet.cli.model import load_model
+from huggingface_hub import hf_hub_download
+import spaces
+
+
+
+
+
+
+REPO_ID = "Revai/reverb-asr"
+files = ['reverb_asr_v1.jit.zip', 'tk.units.txt']
+downloaded_files = [hf_hub_download(repo_id=REPO_ID, filename=f) for f in files]
+model = load_model(downloaded_files[0], downloaded_files[1])
+    
+
+
+def process_cat_embs(cat_embs):
+    device = "gpu"
+    cat_embs = torch.tensor([float(c) for c in cat_embs.split(',')]).to(device)
+    return cat_embs
+
+
+def recognition(audio, style=0):
+    if not audio:
+        return "Input Error! Please enter one audio!"
+    
+    cat_embs = process_cat_embs(f'{style},{1-style}')
+    result = model.transcribe(audio, cat_embs=cat_embs)
+
+    if not result or 'text' not in result:
+        return "ERROR! No text output! Please try again!"
+    
+    text_output = result['text'].replace('▁', ' ')
+    return text_output
+
+
+# Gradio UI Components
+inputs = [
+    gr.Audio(source="microphone", type="filepath", label='Input audio'),
+    gr.Slider(0, 1, value=0, label="Transcription Style", info="Adjust between non-verbatim (0) and verbatim (1) transcription")
+]
+
+output = gr.Textbox(label="Output Text")
+
+# UI and Interface
+iface = gr.Interface(
+    fn=recognition,
+    inputs=inputs,
+    outputs=output,
+    title="Reverb ASR Transcription",
+    description="Supports verbatim and non-verbatim transcription styles.",
+    article="<p style='text-align: center'><a href='https://rev.com' target='_blank'>Learn more about Rev</a></p>",
+    theme='huggingface'
+)
+
+iface.launch(enable_queue=True)