Create fn.py

fn.py

@@ -0,0 +1,39 @@
+import torch
+from faster_whisper import WhisperModel
+
+model = None
+model_size = None
+
+def load_model(_model_size):
+    global model_size, model
+
+    if _model_size and model_size != _model_size:
+        model_size = _model_size
+
+    if torch.cuda.is_available():
+        model = WhisperModel(model_size, device="cuda", compute_type="float16")
+        # model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
+    else:
+        model = WhisperModel(model_size, device="cpu", compute_type="int8")
+
+def speech_to_text(audio_file, _model_size = None):
+    global model_size, model
+
+    load_model(_model_size)
+
+    with torch.no_grad():
+        segments, info = model.transcribe(
+            audio_file,
+            language='ja',
+            beam_size=5,
+            vad_filter=True,
+            without_timestamps=False,
+        )
+
+    text_only = ''
+    text_with_timestamps = ''
+    for segment in segments:
+        text_only += f"{segment.text}\n"
+        text_with_timestamps += f"{segment.start:.2f}\t{segment.end:.2f}\t{segment.text}\n"
+
+    return text_only, text_with_timestamps