fix the formats

python/ctranslate2/converters/transformers.py

@@ -1022,10 +1022,11 @@ def set_feature_extractor(self, spec, feature_extractor):
         spec.feat_layer0.conv.weight = feature_extractor.conv_layers[0].conv.weight
         spec.feat_layer0.conv.bias = feature_extractor.conv_layers[0].conv.bias
         self.set_layer_norm(
-            spec.feat_layer0.layer_norm,
-            feature_extractor.conv_layers[0].layer_norm
+            spec.feat_layer0.layer_norm, feature_extractor.conv_layers[0].layer_norm
         )
-        for spec_layer, module_layer in zip(spec.feat_layer, feature_extractor.conv_layers[1:]):
+        for spec_layer, module_layer in zip(
+            spec.feat_layer, feature_extractor.conv_layers[1:]
+        ):
             spec_layer.conv.weight = module_layer.conv.weight
             spec_layer.conv.bias = module_layer.conv.bias
             self.set_layer_norm(spec_layer.layer_norm, module_layer.layer_norm)
@@ -1037,7 +1038,9 @@ def set_feature_projection(self, spec, feature_projection):
     def set_pos_conv_embed(self, spec, encoder, config):
         # forcing parameters to be set because some transformers version initializes garbage numbers
         # conv parameters are float16 so force float32 for the loading
-        encoder.pos_conv_embed.conv.weight.data = encoder.pos_conv_embed.conv.weight.data.float()
+        encoder.pos_conv_embed.conv.weight.data = (
+            encoder.pos_conv_embed.conv.weight.data.float()
+        )
         encoder.pos_conv_embed.conv.bias.data = encoder.pos_conv_embed.conv.bias.float()
         for param in encoder.pos_conv_embed.parameters():
             param.data = param.data.float()

python/ctranslate2/specs/wav2vec2_spec.py

@@ -16,11 +16,7 @@ def __init__(self):
 class Wav2Vec2Spec(model_spec.LanguageModelSpec):
     def __init__(self, feat_layers, num_layers, num_heads):
         super().__init__()
-        self.encoder = Wav2Vec2EncoderSpec(
-            feat_layers,
-            num_layers,
-            num_heads
-        )
+        self.encoder = Wav2Vec2EncoderSpec(feat_layers, num_layers, num_heads)
 
     @property
     def name(self):
@@ -52,9 +48,7 @@ class Wav2Vec2EncoderSpec(model_spec.LayerSpec):
     def __init__(self, feat_layers, num_layers, num_heads):
         self.num_heads = np.dtype("int16").type(num_heads)
         self.feat_layer0 = Wav2Vec2LayerNormConvLayer()
-        self.feat_layer = [
-            Wav2Vec2LayerNormConvLayer() for i in range(feat_layers - 1)
-        ]
+        self.feat_layer = [Wav2Vec2LayerNormConvLayer() for i in range(feat_layers - 1)]
         self.fp_layer_norm = common_spec.LayerNormSpec()
         self.fp_projection = common_spec.LinearSpec()
         self.pos_conv_embed = Wav2Vec2PosEmbedConvLayer()

python/tests/test_transformers.py

@@ -982,7 +982,9 @@ def test_transformers_wav2vec2(
 
         w2v2_processor = transformers.Wav2Vec2Processor.from_pretrained(model_name)
         w2v2_processor.save_pretrained(output_dir + "/wav2vec2_processor")
-        processor = transformers.AutoProcessor.from_pretrained(output_dir + "/wav2vec2_processor")
+        processor = transformers.AutoProcessor.from_pretrained(
+            output_dir + "/wav2vec2_processor"
+        )
 
         device = "cuda" if os.environ.get("CUDA_VISIBLE_DEVICES") else "cpu"
         cpu_threads = int(os.environ.get("OMP_NUM_THREADS", 0))
@@ -1007,12 +1009,14 @@ def test_transformers_wav2vec2(
 
         hidden_states = np.ascontiguousarray(input_values.unsqueeze(0))
         hidden_states = ctranslate2.StorageView.from_array(hidden_states)
-        to_cpu = (model.device == "cuda" and len(model.device_index) > 1)
+        to_cpu = model.device == "cuda" and len(model.device_index) > 1
         output = model.encode(hidden_states, to_cpu=to_cpu)
         if model.device == "cuda":
             logits = torch.as_tensor(output, device=model.device)[0]
         else:
-            logits = torch.as_tensor(np.array(output), dtype=torch.float32, device=model.device)[0]
+            logits = torch.as_tensor(
+                np.array(output), dtype=torch.float32, device=model.device
+            )[0]
 
         predicted_ids = torch.argmax(logits, dim=-1)
         transcription = processor.decode(predicted_ids, output_word_offsets=True)