dnd_transcribe/argparse.py

@@ -19,12 +19,6 @@ def build_argument_parser() -> argparse.ArgumentParser:
         action="store_false",
         help="Disable using the GPU with CUDA",
     )
-    parser.add_argument(
-        "--block-len",
-        type=int,
-        default=30,
-        help="Block length in seconds of audio sent when streaming or to whisper",
-    )
     parser.add_argument(
         "-m",
         "--model",

dnd_transcribe/inference.py

@@ -25,11 +25,8 @@ class InferredTranscript(typing.NamedTuple):
 
 
 class Inference:
-    def __init__(
-        self, model_name: str, block_len: int = 20, use_gpu: bool = True
-    ) -> None:
+    def __init__(self, model_name: str, use_gpu: bool = True) -> None:
         self.model_name = model_name
-        self.block_len = block_len
         cuda_available = use_gpu and torch.cuda.is_available()
         self.device = "cuda" if cuda_available else "cpu"
         self.torch_dtype = torch.float16 if cuda_available else torch.float32
@@ -55,7 +52,7 @@ class Inference:
             raise Exception(f"Unsupported sample rate {samplerate}")
         stream = librosa.stream(
             audio_file_path,
-            block_length=self.block_len,
+            block_length=20,
             frame_length=AUDIO_SAMPLE_RATE,
             hop_length=AUDIO_SAMPLE_RATE,
         )
@@ -65,18 +62,14 @@ class Inference:
         for block in stream:
             if len(block.shape) > 1:
                 block = speech[:, 0] + speech[:, 1]
-            try:
-                block_inference = self.buffer_to_text(block)
-                transcript += block_inference.transcript + " "
-                processing_time += block_inference.processing_time_sec
-                if block_inference.confidence_score is not None:
-                    if confidence is None:
-                        confidence = block_inference.confidence_score
-                    else:
-                        confidence *= block_inference.confidence_score
-            except torch.OutOfMemoryError as e:
-                print(e)
-                break
+            block_inference = self.buffer_to_text(block)
+            transcript += block_inference.transcript + " "
+            processing_time += block_inference.processing_time_sec
+            if block_inference.confidence_score is not None:
+                if confidence is None:
+                    confidence = block_inference.confidence_score
+                else:
+                    confidence *= block_inference.confidence_score
         return InferredTranscript(transcript.strip(), confidence, processing_time)
 
     def file_to_text(self, audio_file: typing.BinaryIO) -> InferredTranscript:
@@ -129,7 +122,7 @@ class Inference:
             model=self.model,
             tokenizer=self.processor.tokenizer,
             feature_extractor=self.processor.feature_extractor,
-            chunk_length_s=self.block_len,
+            chunk_length_s=30,
             batch_size=16,  # batch size for inference - set based on your device
             torch_dtype=self.torch_dtype,
             device=self.device,

dnd_transcribe/main.py

@@ -13,11 +13,7 @@ def main():
         logging.basicConfig(level=logging.ERROR, format=logging_format)
     else:
         logging.basicConfig(level=logging.INFO, format=logging_format)
-    inference = dnd_transcribe.inference.Inference(
-        args.model,
-        block_len=args.block_len,
-        use_gpu=args.use_gpu,
-    )
+    inference = dnd_transcribe.inference.Inference(args.model, use_gpu=args.use_gpu)
     if args.audio_file is not None:
         _print_inferred_transcript(inference.file_to_text(args.audio_file))
     elif args.stream_audio_file is not None: