Adding support for fp16 for asr pipeline.

src/transformers/pipelines/__init__.py

@@ -874,6 +874,9 @@ def pipeline(
     if feature_extractor is not None:
         kwargs["feature_extractor"] = feature_extractor
 
+    if torch_dtype is not None:
+        kwargs["torch_dtype"] = torch_dtype
+
     if device is not None:
         kwargs["device"] = device
 

src/transformers/pipelines/automatic_speech_recognition.py

@@ -52,13 +52,15 @@ def rescale_stride(stride, ratio):
     return new_strides
 
 
-def chunk_iter(inputs, feature_extractor, chunk_len, stride_left, stride_right):
+def chunk_iter(inputs, feature_extractor, chunk_len, stride_left, stride_right, dtype=None):
     inputs_len = inputs.shape[0]
     step = chunk_len - stride_left - stride_right
     for i in range(0, inputs_len, step):
         # add start and end paddings to the chunk
         chunk = inputs[i : i + chunk_len]
         processed = feature_extractor(chunk, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt")
+        if dtype is not None:
+            processed = processed.to(dtype=dtype)
         _stride_left = 0 if i == 0 else stride_left
         is_last = i + step + stride_left >= inputs_len
         _stride_right = 0 if is_last else stride_right
@@ -240,6 +242,8 @@ def _sanitize_parameters(self, **kwargs):
             preprocess_params["stride_length_s"] = kwargs["stride_length_s"]
         if "ignore_warning" in kwargs:
             preprocess_params["ignore_warning"] = kwargs["ignore_warning"]
+        if "torch_dtype" in kwargs:
+            preprocess_params["dtype"] = kwargs["torch_dtype"]
 
         postprocess_params = {}
         if "decoder_kwargs" in kwargs:
@@ -249,7 +253,7 @@ def _sanitize_parameters(self, **kwargs):
 
         return preprocess_params, {}, postprocess_params
 
-    def preprocess(self, inputs, chunk_length_s=0, stride_length_s=None, ignore_warning=False):
+    def preprocess(self, inputs, chunk_length_s=0, stride_length_s=None, ignore_warning=False, dtype=None):
         if isinstance(inputs, str):
             if inputs.startswith("http://") or inputs.startswith("https://"):
                 # We need to actually check for a real protocol, otherwise it's impossible to use a local file
@@ -332,12 +336,14 @@ def preprocess(self, inputs, chunk_length_s=0, stride_length_s=None, ignore_warn
                 raise ValueError("Chunk length must be superior to stride length")
 
             # make sure that
-            for item in chunk_iter(inputs, self.feature_extractor, chunk_len, stride_left, stride_right):
+            for item in chunk_iter(inputs, self.feature_extractor, chunk_len, stride_left, stride_right, dtype):
                 yield item
         else:
             processed = self.feature_extractor(
                 inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"
             )
+            if dtype is not None:
+                processed = processed.to(dtype=dtype)
             if stride is not None:
                 if self.model.__class__ in MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.values():
                     raise ValueError("Stride is only usable with CTC models, try removing it")
@@ -366,6 +372,7 @@ def _forward(self, model_inputs):
             # `generate` magic to create the mask automatically won't work, we basically need to help
             # it here.
             attention_mask = model_inputs.pop("attention_mask", None)
+
             tokens = self.model.generate(
                 encoder_outputs=encoder(inputs, attention_mask=attention_mask),
                 attention_mask=attention_mask,

tests/pipelines/test_pipelines_automatic_speech_recognition.py

@@ -145,6 +145,19 @@ def test_small_model_pt(self):
         with self.assertRaisesRegex(ValueError, "^We cannot return_timestamps yet on non-ctc models !$"):
             _ = speech_recognizer(waveform, return_timestamps="char")
 
+    @slow
+    @require_torch
+    def test_whisper_fp16(self):
+        if not torch.cuda.is_available():
+            self.skipTest("Cuda is necessary for this test")
+        speech_recognizer = pipeline(
+            model="openai/whisper-base",
+            device=0,
+            torch_dtype=torch.float16,
+        )
+        waveform = np.tile(np.arange(1000, dtype=np.float32), 34)
+        speech_recognizer(waveform)
+
     @require_torch
     def test_small_model_pt_seq2seq(self):
         speech_recognizer = pipeline(