Update README.md

README.md

@@ -7,6 +7,8 @@ metrics:
 model-index:
 - name: weights
   results: []
+datasets:
+- librispeech_asr
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -19,6 +21,109 @@ It achieves the following results on the evaluation set:
 - Loss: 0.0061
 - F1: 0.9993
 
+### Compute your inferences
+
+```python
+class DataColletor:
+    def __init__(
+        self,
+        processor: Wav2Vec2Processor,
+        sampling_rate: int = 16000,
+        padding: Union[bool, str] = True,
+        max_length: Optional[int] = None,
+        pad_to_multiple_of: Optional[int] = None,
+        label2id: Dict = None,
+        max_audio_len: int = 5
+    ):
+
+        self.processor = processor
+        self.sampling_rate = sampling_rate
+
+        self.padding = padding
+        self.max_length = max_length
+        self.pad_to_multiple_of = pad_to_multiple_of
+
+        self.label2id = label2id
+
+        self.max_audio_len = max_audio_len
+
+    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+        # split inputs and labels since they have to be of different lenghts and need
+        # different padding methods
+        input_features = []
+        label_features = []
+        for feature in features:
+            speech_array, sampling_rate = torchaudio.load(feature["input_values"])
+
+            # Transform to Mono
+            speech_array = torch.mean(speech_array, dim=0, keepdim=True)
+
+            if sampling_rate != self.sampling_rate:
+                transform = torchaudio.transforms.Resample(sampling_rate, self.sampling_rate)
+                speech_array = transform(speech_array)
+                sampling_rate = self.sampling_rate
+
+            effective_size_len = sampling_rate * self.max_audio_len
+
+            if speech_array.shape[-1] > effective_size_len:
+                speech_array = speech_array[:, :effective_size_len]
+
+            speech_array = speech_array.squeeze().numpy()
+            input_tensor = self.processor(speech_array, sampling_rate=sampling_rate).input_values
+            input_tensor = np.squeeze(input_tensor)
+
+            input_features.append({"input_values": input_tensor})
+
+        batch = self.processor.pad(
+            input_features,
+            padding=self.padding,
+            max_length=self.max_length,
+            pad_to_multiple_of=self.pad_to_multiple_of,
+            return_tensors="pt",
+        )
+
+        return batch
+
+
+label2id = {
+    "female": 0,
+    "male": 1
+}
+
+id2label = {
+    0: "female",
+    1: "male"
+}
+
+num_labels = 2
+
+feature_extractor = AutoFeatureExtractor.from_pretrained("alefiury/wav2vec2-large-xlsr-53-gender-recognition-librispeech")
+model = AutoModelForAudioClassification.from_pretrained(
+    pretrained_model_name_or_path="alefiury/wav2vec2-large-xlsr-53-gender-recognition-librispeech",
+    num_labels=num_labels,
+    label2id=label2id,
+    id2label=id2label,
+)
+
+data_collator = DataColletorTrain(
+    feature_extractor,
+    sampling_rate=16000,
+    padding=True,
+    label2id=label2id
+)
+
+test_dataloader = DataLoader(
+    dataset=test_dataset,
+    batch_size=16,
+    collate_fn=data_collator,
+    shuffle=False,
+    num_workers=10
+)
+
+preds = predict(test_dataloader=test_dataloader, model=model)
+```
+
+
 ## Training and evaluation data
 
 The Librispeech-clean-100 dataset was used to train the model, with 70% of the data used for training, 10% for validation, and 20% for testing.
@@ -49,4 +154,4 @@ The following hyperparameters were used during training:
 
 - Transformers 4.28.0
 - Pytorch 2.0.0+cu118
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3