Accept torch.Tensor as input

whisperx/asr.py

@@ -74,11 +74,13 @@ def decode_batch(tokens: List[List[int]]) -> str:
 
         return text
 
-    def encode(self, features: np.ndarray) -> ctranslate2.StorageView:
+    def encode(self, features: Union[np.ndarray, torch.Tensor]) -> 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
         # unsqueeze if batch size = 1
+        if isinstance(features, torch.Tensor):
+            features = features.cpu().numpy()
         if len(features.shape) == 2:
             features = np.expand_dims(features, 0)
         features = faster_whisper.transcribe.get_ctranslate2_storage(features)
@@ -171,19 +173,22 @@ def stack(items):
         return final_iterator
 
     def transcribe(
-        self, audio: Union[str, np.ndarray], batch_size=None, num_workers=0, language=None, task=None, chunk_size=30, print_progress = False, combined_progress=False
+        self, audio: Union[str, np.ndarray, torch.Tensor], batch_size=None, num_workers=0, language=None, task=None, chunk_size=30, print_progress = False, combined_progress=False
     ) -> TranscriptionResult:
         if isinstance(audio, str):
             audio = load_audio(audio)
 
+        if isinstance(audio, np.ndarray):
+            audio = torch.from_numpy(audio)
+
         def data(audio, segments):
             for seg in segments:
                 f1 = int(seg['start'] * SAMPLE_RATE)
                 f2 = int(seg['end'] * SAMPLE_RATE)
                 # print(f2-f1)
                 yield {'inputs': audio[f1:f2]}
 
-        vad_segments = self.vad_model({"waveform": torch.from_numpy(audio).unsqueeze(0), "sample_rate": SAMPLE_RATE})
+        vad_segments = self.vad_model({"waveform": audio.unsqueeze(0), "sample_rate": SAMPLE_RATE})
         vad_segments = merge_chunks(
             vad_segments,
             chunk_size,
@@ -203,7 +208,7 @@ def data(audio, segments):
                 self.tokenizer = faster_whisper.tokenizer.Tokenizer(self.model.hf_tokenizer,
                                                                     self.model.model.is_multilingual, task=task,
                                                                     language=language)
-                
+
         if self.suppress_numerals:
             previous_suppress_tokens = self.options.suppress_tokens
             numeral_symbol_tokens = find_numeral_symbol_tokens(self.tokenizer)
@@ -242,7 +247,7 @@ def data(audio, segments):
         return {"segments": segments, "language": language}
 
 
-    def detect_language(self, audio: np.ndarray):
+    def detect_language(self, audio: Union[np.ndarray, torch.Tensor]):
         if audio.shape[0] < N_SAMPLES:
             print("Warning: audio is shorter than 30s, language detection may be inaccurate.")
         model_n_mels = self.model.feat_kwargs.get("feature_size")

whisperx/diarize.py

@@ -18,11 +18,17 @@ def __init__(
             device = torch.device(device)
         self.model = Pipeline.from_pretrained(model_name, use_auth_token=use_auth_token).to(device)
 
-    def __call__(self, audio: Union[str, np.ndarray], num_speakers=None, min_speakers=None, max_speakers=None):
+    def __call__(self, audio: Union[str, np.ndarray, torch.Tensor], num_speakers=None, min_speakers=None, max_speakers=None):
         if isinstance(audio, str):
             audio = load_audio(audio)
+
+        audio = audio[None, :]
+
+        if isinstance(audio, np.ndarray):
+            audio = torch.from_numpy(audio)
+
         audio_data = {
-            'waveform': torch.from_numpy(audio[None, :]),
+            'waveform': audio,
             'sample_rate': SAMPLE_RATE
         }
         segments = self.model(audio_data, num_speakers = num_speakers, min_speakers=min_speakers, max_speakers=max_speakers)
@@ -47,7 +53,7 @@ def assign_word_speakers(diarize_df, transcript_result, fill_nearest=False):
             # sum over speakers
             speaker = dia_tmp.groupby("speaker")["intersection"].sum().sort_values(ascending=False).index[0]
             seg["speaker"] = speaker
-        
+
         # assign speaker to words
         if 'words' in seg:
             for word in seg['words']:
@@ -63,8 +69,8 @@ def assign_word_speakers(diarize_df, transcript_result, fill_nearest=False):
                         # sum over speakers
                         speaker = dia_tmp.groupby("speaker")["intersection"].sum().sort_values(ascending=False).index[0]
                         word["speaker"] = speaker
-        
-    return transcript_result            
+
+    return transcript_result
 
 
 class Segment: