Fix

whispering/cli.py

@@ -48,16 +48,16 @@ def transcribe_from_mic(
     ):
         idx: int = 0
         while True:
-            logger.debug(f"Segment #: {idx}, The rest of queue: {q.qsize()}")
+            logger.debug(f"Audio #: {idx}, The rest of queue: {q.qsize()}")
 
             if no_progress:
-                segment = q.get()
+                audio = q.get()
             else:
                 pbar_thread = ProgressBar(
                     num_block=num_block,  # TODO: set more accurate value
                 )
                 try:
-                    segment = q.get()
+                    audio = q.get()
                 except KeyboardInterrupt:
                     pbar_thread.kill()
                     return
@@ -68,7 +68,7 @@ def transcribe_from_mic(
                 sys.stderr.write("Analyzing")
                 sys.stderr.flush()
 
-            for chunk in wsp.transcribe(segment=segment, ctx=ctx):
+            for chunk in wsp.transcribe(audio=audio, ctx=ctx):
                 if not no_progress:
                     sys.stderr.write("\r")
                     sys.stderr.flush()

whispering/schema.py

@@ -2,6 +2,7 @@
 
 from typing import List, Optional
 
+import numpy as np
 import torch
 from pydantic import BaseModel, root_validator
 
@@ -55,4 +56,4 @@ class ParsedChunk(BaseModel):
 class SpeechSegment(BaseModel, arbitrary_types_allowed=True):
     start_block_idx: int
     end_block_idx: int
-    segment: torch.Tensor
+    audio: np.ndarray

whispering/transcriber.py

@@ -3,6 +3,7 @@
 from logging import getLogger
 from typing import Final, Iterator, Optional, Union
 
+import numpy as np
 import torch
 from whisper import Whisper, load_model
 from whisper.audio import (
@@ -226,13 +227,13 @@ class WhisperStreamingTranscriber:
     def transcribe(
         self,
         *,
-        segment: torch.Tensor,
+        audio: np.ndarray,
         ctx: Context,
     ) -> Iterator[ParsedChunk]:
-        for speech_segment in self.vad(segment=segment):
+        for speech_segment in self.vad(audio=audio):
             logger.debug(f"{speech_segment}")
 
-        new_mel = log_mel_spectrogram(audio=segment)
+        new_mel = log_mel_spectrogram(audio=audio)
         logger.debug(f"Incoming new_mel.shape: {new_mel.shape}")
         if ctx.buffer_mel is None:
             mel = new_mel
@@ -244,7 +245,7 @@ class WhisperStreamingTranscriber:
 
         seek: int = 0
         while seek < mel.shape[-1]:
-            segment = (
+            segment: torch.Tensor = (
                 pad_or_trim(mel[:, seek:], N_FRAMES)
                 .to(self.model.device)  # type: ignore
                 .to(self.dtype)

whispering/vad.py

@@ -2,6 +2,7 @@
 
 from typing import Iterator
 
+import numpy as np
 import torch
 from whisper.audio import N_FRAMES, SAMPLE_RATE
 
@@ -20,10 +21,10 @@ class VAD:
     def __call__(
         self,
         *,
-        segment: torch.Tensor,
+        audio: np.ndarray,
         thredhold: float = 0.5,
     ) -> Iterator[SpeechSegment]:
-        # segment.shape should be multiple of (N_FRAMES,)
+        # audio.shape should be multiple of (N_FRAMES,)
 
         def my_ret(
             *,
@@ -33,17 +34,17 @@ class VAD:
             return SpeechSegment(
                 start_block_idx=start_block_idx,
                 end_block_idx=idx,
-                segment=segment[N_FRAMES * start_block_idx : N_FRAMES * idx],
+                audio=audio[N_FRAMES * start_block_idx : N_FRAMES * idx],
             )
 
-        block_size: int = int(segment.shape[0] / N_FRAMES)
+        block_size: int = int(audio.shape[0] / N_FRAMES)
 
         start_block_idx = None
         for idx in range(block_size):
             start: int = N_FRAMES * idx
             end: int = N_FRAMES * (idx + 1)
             vad_prob = self.vad_model(
-                torch.from_numpy(segment[start:end]),
+                torch.from_numpy(audio[start:end]),
                 SAMPLE_RATE,
             ).item()
             if vad_prob > thredhold: