. [skip ci]

faster_whisper/transcribe.py

@@ -119,6 +119,58 @@ def __init__(
         self.model: WhisperModel = model
         self.last_speech_timestamp = 0.0
 
+    def forward(self, features, tokenizer, chunks_metadata, options):
+        encoder_output, outputs = self.generate_segment_batched(
+            features, tokenizer, options
+        )
+
+        segmented_outputs = []
+        segment_sizes = []
+        for chunk_metadata, output in zip(chunks_metadata, outputs):
+            duration = chunk_metadata["end_time"] - chunk_metadata["start_time"]
+            segment_size = int(ceil(duration) * self.model.frames_per_second)
+            segment_sizes.append(segment_size)
+            (
+                subsegments,
+                seek,
+                single_timestamp_ending,
+            ) = self.model._split_segments_by_timestamps(
+                tokenizer=tokenizer,
+                tokens=output["tokens"],
+                time_offset=chunk_metadata["start_time"],
+                segment_size=segment_size,
+                segment_duration=duration,
+                seek=0,
+            )
+            segmented_outputs.append(
+                [
+                    dict(
+                        text=tokenizer.decode(subsegment["tokens"]),
+                        avg_logprob=output["avg_logprob"],
+                        no_speech_prob=output["no_speech_prob"],
+                        tokens=subsegment["tokens"],
+                        start=subsegment["start"],
+                        end=subsegment["end"],
+                        compression_ratio=get_compression_ratio(
+                            tokenizer.decode(subsegment["tokens"])
+                        ),
+                    )
+                    for subsegment in subsegments
+                ]
+            )
+        if options.word_timestamps:
+            self.last_speech_timestamp = self.model.add_word_timestamps(
+                segmented_outputs,
+                tokenizer,
+                encoder_output,
+                segment_sizes,
+                options.prepend_punctuations,
+                options.append_punctuations,
+                self.last_speech_timestamp,
+            )
+
+        return segmented_outputs
+
     def generate_segment_batched(
         self,
         features: np.ndarray,
@@ -223,9 +275,6 @@ def forward(self, features, tokenizer, chunks_metadata, options):
                         compression_ratio=get_compression_ratio(
                             tokenizer.decode(subsegment["tokens"])
                         ),
-                        seek=int(
-                            chunk_metadata["start_time"] * self.model.frames_per_second
-                        ),
                     )
                     for subsegment in subsegments
                 ]
@@ -542,7 +591,7 @@ def transcribe(
             vad_options=vad_parameters,
             all_language_probs=all_language_probs,
         )
-        
+
         audio_chunks, chunks_metadata = collect_chunks(audio, clip_timestamps)
         features = (
             np.stack(