Support VAD filter (#95)

* Support VAD filter

* Generalize function collect_samples

* Define AudioSegment class

* Only pass prompt and prefix to the first chunk

* Add dict argument vad_parameters

* Fix isort format

* Rename method

* Update README

* Add shortcut when the chunk offset is 0

* Reword readme

* Fix end property

* Concatenate the speech chunks

* Cleanup diff

* Increase default speech pad

* Update README

* Increase default speech pad

faster_whisper/transcribe.py

@@ -12,6 +12,11 @@ from faster_whisper.audio import decode_audio
 from faster_whisper.feature_extractor import FeatureExtractor
 from faster_whisper.tokenizer import Tokenizer
 from faster_whisper.utils import download_model
+from faster_whisper.vad import (
+    SpeechTimestampsMap,
+    collect_chunks,
+    get_speech_timestamps,
+)
 
 
 class Word(NamedTuple):
@@ -152,6 +157,8 @@ class WhisperModel:
         word_timestamps: bool = False,
         prepend_punctuations: str = "\"'“¿([{-",
         append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
+        vad_filter: bool = False,
+        vad_parameters: Optional[dict] = None,
     ) -> Tuple[Iterable[Segment], AudioInfo]:
         """Transcribes an input file.
 
@@ -192,6 +199,11 @@ class WhisperModel:
             with the next word
           append_punctuations: If word_timestamps is True, merge these punctuation symbols
             with the previous word
+          vad_filter: Enable the voice activity detection (VAD) to filter out parts of the audio
+            without speech. This step is using the Silero VAD model
+            https://github.com/snakers4/silero-vad.
+          vad_parameters: Dictionary of Silero VAD parameters (see available parameters and
+            default values in the function `get_speech_timestamps`).
 
         Returns:
           A tuple with:
@@ -205,6 +217,14 @@ class WhisperModel:
             )
 
         duration = audio.shape[0] / self.feature_extractor.sampling_rate
+
+        if vad_filter:
+            vad_parameters = {} if vad_parameters is None else vad_parameters
+            speech_chunks = get_speech_timestamps(audio, **vad_parameters)
+            audio = collect_chunks(audio, speech_chunks)
+        else:
+            speech_chunks = None
+
         features = self.feature_extractor(audio)
 
         encoder_output = None
@@ -254,6 +274,11 @@ class WhisperModel:
 
         segments = self.generate_segments(features, tokenizer, options, encoder_output)
 
+        if speech_chunks:
+            segments = restore_speech_timestamps(
+                segments, speech_chunks, self.feature_extractor.sampling_rate
+            )
+
         audio_info = AudioInfo(
             language=language,
             language_probability=language_probability,
@@ -678,6 +703,36 @@ class WhisperModel:
         ]
 
 
+def restore_speech_timestamps(
+    segments: Iterable[Segment],
+    speech_chunks: List[dict],
+    sampling_rate: int,
+) -> Iterable[Segment]:
+    ts_map = SpeechTimestampsMap(speech_chunks, sampling_rate)
+
+    for segment in segments:
+        if segment.words:
+            words = []
+            for word in segment.words:
+                # Ensure the word start and end times are resolved to the same chunk.
+                chunk_index = ts_map.get_chunk_index(word.start)
+                word = word._replace(
+                    start=ts_map.get_original_time(word.start, chunk_index),
+                    end=ts_map.get_original_time(word.end, chunk_index),
+                )
+                words.append(word)
+        else:
+            words = segment.words
+
+        segment = segment._replace(
+            start=ts_map.get_original_time(segment.start),
+            end=ts_map.get_original_time(segment.end),
+            words=words,
+        )
+
+        yield segment
+
+
 def get_ctranslate2_storage(segment: np.ndarray) -> ctranslate2.StorageView:
     segment = np.ascontiguousarray(segment)
     segment = ctranslate2.StorageView.from_array(segment)