Merge branch 'stream'

dnd_transcribe/argparse.py

@@ -1,4 +1,5 @@
 import argparse
+import os
 from dnd_transcribe.inference import DEFAULT_MODEL
 
 
@@ -18,6 +19,12 @@ 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",
@@ -29,6 +36,19 @@ def build_argument_parser() -> argparse.ArgumentParser:
         "-f",
         "--audio-file",
         type=argparse.FileType(mode="rb"),
-        help="Audio file to process",
+        help="Audio file to process, for long audo see --stream-audio-file",
+    )
+    parser.add_argument(
+        "-s",
+        "--stream-audio-file",
+        type=valid_file_path,
+        help="Audio file to process by streaming",
     )
     return parser
+
+
+def valid_file_path(path: str) -> str:
+    path = os.path.realpath(path)
+    if os.path.isfile(path):
+        return path
+    raise argparse.ArgumentTypeError("{} is not a valid file".format(path))

dnd_transcribe/inference.py

@@ -1,4 +1,4 @@
-import soundfile
+import librosa
 import numpy.typing
 import time
 import torch
@@ -25,8 +25,11 @@ class InferredTranscript(typing.NamedTuple):
 
 
 class Inference:
-    def __init__(self, model_name: str, use_gpu: bool = True) -> None:
+    def __init__(
+        self, model_name: str, block_len: int = 20, 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
@@ -46,10 +49,42 @@ class Inference:
     def is_wav2vec2(self) -> bool:
         return self.model_name.startswith("facebook/wav2vec2")
 
-    def file_to_text(self, audio_file: typing.BinaryIO) -> InferredTranscript:
+    def stream_file_to_text(self, audio_file_path: str) -> InferredTranscript:
-        audio_input, samplerate = soundfile.read(audio_file)
+        samplerate = librosa.get_samplerate(audio_file_path)
         if samplerate != AUDIO_SAMPLE_RATE:
             raise Exception(f"Unsupported sample rate {samplerate}")
+        stream = librosa.stream(
+            audio_file_path,
+            block_length=self.block_len,
+            frame_length=AUDIO_SAMPLE_RATE,
+            hop_length=AUDIO_SAMPLE_RATE,
+        )
+        transcript = ""
+        confidence = None
+        processing_time = 0.0
+        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
+        return InferredTranscript(transcript.strip(), confidence, processing_time)
+
+    def file_to_text(self, audio_file: typing.BinaryIO) -> InferredTranscript:
+        audio_input, samplerate = librosa.load(audio_file)
+        if samplerate != AUDIO_SAMPLE_RATE:
+            audio_input = librosa.resample(
+                audio_input, orig_sr=samplerate, target_sr=AUDIO_SAMPLE_RATE
+            )
         return self.buffer_to_text(audio_input)
 
     def buffer_to_text(
@@ -94,7 +129,7 @@ class Inference:
             model=self.model,
             tokenizer=self.processor.tokenizer,
             feature_extractor=self.processor.feature_extractor,
-            chunk_length_s=30,
+            chunk_length_s=self.block_len,
             batch_size=16,  # batch size for inference - set based on your device
             torch_dtype=self.torch_dtype,
             device=self.device,

dnd_transcribe/main.py

@@ -13,13 +13,27 @@ 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, use_gpu=args.use_gpu)
+    inference = dnd_transcribe.inference.Inference(
+        args.model,
+        block_len=args.block_len,
+        use_gpu=args.use_gpu,
+    )
     if args.audio_file is not None:
-        (transcription, score, duration) = inference.file_to_text(args.audio_file)
+        _print_inferred_transcript(inference.file_to_text(args.audio_file))
-        print(transcription)
+    elif args.stream_audio_file is not None:
-        if score is not None:
+        _print_inferred_transcript(
-            print(f"[Confidence: {score:.1%} in {duration} seconds]")
+            inference.stream_file_to_text(args.stream_audio_file)
-        else:
+        )
-            print(f"[Confidence -unknown- in {duration} seconds]")
     else:
         print("Live transcription is a WIP")
+
+
+def _print_inferred_transcript(
+    transcript: dnd_transcribe.inference.InferredTranscript,
+) -> None:
+    (transcription, score, duration) = transcript
+    print(transcription)
+    if score is not None:
+        print(f"[Confidence: {score:.1%} in {duration} seconds]")
+    else:
+        print(f"[Confidence -unknown- in {duration} seconds]")

requirements.txt

@@ -1,3 +1,4 @@
-soundfile>=0.13.1
+librosa>=0.11.0
+numpy>=2.2.5
 torch>=2.6.0
 transformers>=4.51.3