Initial commit

transcription-api/transcribe.py

@@ -0,0 +1,56 @@
+from backends.fasterwhisper import FasterWhisperBackend
+from backends.backend import Transcription
+from faster_whisper import decode_audio
+from models import DeviceType
+from typing import Optional
+import numpy as np
+import io
+import os
+
+def convert_audio(file) -> np.ndarray:
+        return decode_audio(file, split_stereo=False, sampling_rate=16000)
+
+async def transcribe_from_filename(filename: str,
+                                    model_size: int,
+                                    language: Optional[str] = None,
+                                    device: DeviceType = DeviceType.cpu) -> Transcription:
+    
+    filepath = os.path.join(os.environ["UPLOAD_DIR"], filename)
+    if not os.path.exists(filepath):
+        raise RuntimeError(f"file not found in {filepath}")
+    audio = convert_audio(filepath)
+    return await transcribe_audio(audio, model_size, language, device)
+
+async def transcribe_file(file: io.BytesIO, 
+                          model_size: int, 
+                          language: Optional[str] = None, 
+                          device: DeviceType = DeviceType.cpu) -> Transcription:
+    contents = await file.read()  # async read
+    if len(contents) < 150 * 1024 * 1024:  # file is smaller than 150MB
+            audio = convert_audio(io.BytesIO(contents))
+    else:
+         # Save the uploaded file temporarily on disk
+        with open(file.filename, 'wb') as f:
+            f.write(contents)
+        # Check if file exists
+        if not os.path.exists(file.filename):
+            raise RuntimeError(f"file not found in {file.filename}")
+        # Corrected to use the function in this file
+        audio = convert_audio(file.filename)
+        os.remove(file.filename)
+    return await transcribe_audio(audio, model_size, language, device)
+
+async def transcribe_audio(audio: np.ndarray, 
+                           model_size: int, 
+                           language: Optional[str] = None, 
+                           device: DeviceType = DeviceType.cpu) -> Transcription:
+    
+    if language == "auto":
+        language = None
+
+    # Load the model
+    model = FasterWhisperBackend(model_size=model_size, device=device)
+    model.get_model()
+    model.load()
+    # Transcribe the file
+    return model.transcribe(audio, silent=True, language=language)