Add progress bar to WhisperModel.transcribe (#1138)

faster_whisper/transcribe.py

@@ -646,6 +646,7 @@ def transcribe(
         audio: Union[str, BinaryIO, np.ndarray],
         language: Optional[str] = None,
         task: str = "transcribe",
+        log_progress: bool = False,
         beam_size: int = 5,
         best_of: int = 5,
         patience: float = 1,
@@ -695,6 +696,7 @@ def transcribe(
             as "en" or "fr". If not set, the language will be detected in the first 30 seconds
             of audio.
           task: Task to execute (transcribe or translate).
+          log_progress: whether to show progress bar or not.
           beam_size: Beam size to use for decoding.
           best_of: Number of candidates when sampling with non-zero temperature.
           patience: Beam search patience factor.
@@ -941,7 +943,9 @@ def transcribe(
             hotwords=hotwords,
         )
 
-        segments = self.generate_segments(features, tokenizer, options, encoder_output)
+        segments = self.generate_segments(
+            features, tokenizer, options, log_progress, encoder_output
+        )
 
         if speech_chunks:
             segments = restore_speech_timestamps(segments, speech_chunks, sampling_rate)
@@ -1041,6 +1045,7 @@ def generate_segments(
         features: np.ndarray,
         tokenizer: Tokenizer,
         options: TranscriptionOptions,
+        log_progress,
         encoder_output: Optional[ctranslate2.StorageView] = None,
     ) -> Iterable[Segment]:
         content_frames = features.shape[-1] - 1
@@ -1083,6 +1088,7 @@ def generate_segments(
             else:
                 all_tokens.extend(options.initial_prompt)
 
+        pbar = tqdm(total=content_duration, unit="seconds", disable=not log_progress)
         last_speech_timestamp = 0.0
         # NOTE: This loop is obscurely flattened to make the diff readable.
         # A later commit should turn this into a simpler nested loop.
@@ -1341,6 +1347,12 @@ def next_words_segment(segments: List[dict]) -> Optional[dict]:
 
                 prompt_reset_since = len(all_tokens)
 
+            pbar.update(
+                (min(content_frames, seek) - previous_seek)
+                * self.feature_extractor.time_per_frame,
+            )
+        pbar.close()
+
     def encode(self, features: np.ndarray) -> ctranslate2.StorageView:
         # When the model is running on multiple GPUs, the encoder output should be moved
         # to the CPU since we don't know which GPU will handle the next job.