Update main.py

main.py

@@ -1,67 +1,67 @@
-# main.py
-
-import os
-import re
-import numpy as np
-from pydub import AudioSegment
-from fastapi import FastAPI, UploadFile, File
-from fastapi.responses import JSONResponse
-from huggingface_hub import login
-from hazm import Normalizer
-import nemo.collections.asr as nemo_asr
-import uvicorn
-
-# Load Hugging Face token
-HF_TOKEN = os.getenv("HF_TOKEN")
-if not HF_TOKEN:
-    raise ValueError("HF_TOKEN environment variable not set. Please provide a valid Hugging Face token.")
-
-login(HF_TOKEN)
-
-# Load model once
-asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_farsi_fastconformer")
-
-normalizer = Normalizer()
-app = FastAPI()
-
-
-def load_audio(audio_file_path):
-    audio = AudioSegment.from_file(audio_file_path)
-    audio = audio.set_channels(1).set_frame_rate(16000)
-    audio_samples = np.array(audio.get_array_of_samples(), dtype=np.float32)
-    audio_samples /= np.max(np.abs(audio_samples))
-    return audio_samples, audio.frame_rate
-
-
-def transcribe_chunk(audio_chunk, model):
-    transcription = model.transcribe([audio_chunk], batch_size=1, verbose=False)
-    return transcription[0].text
-
-
-def transcribe_audio(file_path, model, chunk_size=30 * 16000):
-    waveform, _ = load_audio(file_path)
-    transcriptions = []
-    for start in range(0, len(waveform), chunk_size):
-        end = min(len(waveform), start + chunk_size)
-        transcription = transcribe_chunk(waveform[start:end], model)
-        transcriptions.append(transcription)
-
-    final_transcription = ' '.join(transcriptions)
-    final_transcription = re.sub(' +', ' ', final_transcription)
-    final_transcription = normalizer.normalize(final_transcription)
-
-    return final_transcription
-
-
-@app.post("/transcribe")
-async def transcribe(file: UploadFile = File(...)):
-    try:
-        temp_path = f"/tmp/{file.filename}"
-        with open(temp_path, "wb") as f:
-            f.write(await file.read())
-
-        result = transcribe_audio(temp_path, asr_model)
-        return {"transcription": result}
-    except Exception as e:
-        return JSONResponse(status_code=500, content={"error": str(e)})
-
+# main.py
+
+import os
+import re
+import numpy as np
+from pydub import AudioSegment
+from fastapi import FastAPI, UploadFile, File
+from fastapi.responses import JSONResponse
+from huggingface_hub import login
+from hazm import Normalizer
+import nemo.collections.asr as nemo_asr
+import uvicorn
+
+# Load Hugging Face token
+HF_TOKEN = os.getenv("HF_TOKEN")
+if not HF_TOKEN:
+    raise ValueError("HF_TOKEN environment variable not set. Please provide a valid Hugging Face token.")
+
+login(HF_TOKEN)
+
+# Load model once
+asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("faimlab/stt_fa_fastconformer_hybrid_large_dataset_v30")
+
+normalizer = Normalizer()
+app = FastAPI()
+
+
+def load_audio(audio_file_path):
+    audio = AudioSegment.from_file(audio_file_path)
+    audio = audio.set_channels(1).set_frame_rate(16000)
+    audio_samples = np.array(audio.get_array_of_samples(), dtype=np.float32)
+    audio_samples /= np.max(np.abs(audio_samples))
+    return audio_samples, audio.frame_rate
+
+
+def transcribe_chunk(audio_chunk, model):
+    transcription = model.transcribe([audio_chunk], batch_size=1, verbose=False)
+    return transcription[0].text
+
+
+def transcribe_audio(file_path, model, chunk_size=30 * 16000):
+    waveform, _ = load_audio(file_path)
+    transcriptions = []
+    for start in range(0, len(waveform), chunk_size):
+        end = min(len(waveform), start + chunk_size)
+        transcription = transcribe_chunk(waveform[start:end], model)
+        transcriptions.append(transcription)
+
+    final_transcription = ' '.join(transcriptions)
+    final_transcription = re.sub(' +', ' ', final_transcription)
+    final_transcription = normalizer.normalize(final_transcription)
+
+    return final_transcription
+
+
+@app.post("/transcribe")
+async def transcribe(file: UploadFile = File(...)):
+    try:
+        temp_path = f"/tmp/{file.filename}"
+        with open(temp_path, "wb") as f:
+            f.write(await file.read())
+
+        result = transcribe_audio(temp_path, asr_model)
+        return {"transcription": result}
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": str(e)})
+