Simplify reuse of the encoder output

faster_whisper/transcribe.py

@@ -205,7 +205,7 @@ class WhisperModel:
         duration = audio.shape[0] / self.feature_extractor.sampling_rate
         features = self.feature_extractor(audio)
 
-        whisper_encoder = WhisperEncoder(self.model)
+        encoder_output = None
 
         if language is None:
             if not self.model.is_multilingual:
@@ -213,7 +213,7 @@ class WhisperModel:
                 language_probability = 1
             else:
                 segment = features[:, : self.feature_extractor.nb_max_frames]
-                encoder_output = whisper_encoder(0, segment)
+                encoder_output = self.encode(segment)
                 results = self.model.detect_language(encoder_output)
                 language_token, language_probability = results[0][0]
                 language = language_token[2:-2]
@@ -250,7 +250,7 @@ class WhisperModel:
             append_punctuations=append_punctuations,
         )
 
-        segments = self.generate_segments(features, whisper_encoder, tokenizer, options)
+        segments = self.generate_segments(features, tokenizer, options, encoder_output)
 
         audio_info = AudioInfo(
             language=language,
@@ -263,9 +263,9 @@ class WhisperModel:
     def generate_segments(
         self,
         features: np.ndarray,
-        whisper_encoder: "WhisperEncoder",
         tokenizer: Tokenizer,
         options: TranscriptionOptions,
+        encoder_output: Optional[ctranslate2.StorageView] = None,
     ) -> Iterable[Segment]:
         content_frames = features.shape[-1] - self.feature_extractor.nb_max_frames
         seek = 0
@@ -293,7 +293,8 @@ class WhisperModel:
                 prefix=options.prefix,
             )
 
-            encoder_output = whisper_encoder(seek, segment)
+            if encoder_output is None:
+                encoder_output = self.encode(segment)
 
             result, avg_log_prob, temperature = self.generate_with_fallback(
                 encoder_output, prompt, tokenizer, options
@@ -420,6 +421,8 @@ class WhisperModel:
                     if seek_shift > 0:
                         seek = previous_seek + seek_shift
 
+            encoder_output = None
+
             for segment in current_segments:
                 tokens = segment["tokens"]
                 text = tokenizer.decode(tokens)
@@ -440,6 +443,16 @@ class WhisperModel:
                     ),
                 )
 
+    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.
+        to_cpu = self.model.device == "cuda" and len(self.model.device_index) > 1
+
+        features = np.expand_dims(features, 0)
+        features = get_ctranslate2_storage(features)
+
+        return self.model.encode(features, to_cpu=to_cpu)
+
     def generate_with_fallback(
         self,
         encoder_output: ctranslate2.StorageView,
@@ -661,37 +674,6 @@ class WhisperModel:
         ]
 
 
-class WhisperEncoder:
-    """Helper class to cache and reuse the encoder output."""
-
-    def __init__(self, model: ctranslate2.models.Whisper):
-        self.model = model
-
-        # 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.
-        self.cache_on_cpu = len(model.device_index) > 1
-
-        self.last_seek = -1
-        self.last_output = None
-
-    def __call__(self, seek: int, features: np.ndarray) -> ctranslate2.StorageView:
-        if self.last_seek == seek:
-            return self.last_output
-
-        features = np.expand_dims(features, 0)
-        features = get_ctranslate2_storage(features)
-
-        output = self.model.encode(features, to_cpu=self.cache_on_cpu)
-
-        if self.last_output is not None:
-            del self.last_output
-
-        self.last_seek = seek
-        self.last_output = output
-
-        return output
-
-
 def get_ctranslate2_storage(segment: np.ndarray) -> ctranslate2.StorageView:
     segment = np.ascontiguousarray(segment)
     segment = ctranslate2.StorageView.from_array(segment)