Update README.md

README.md

@@ -53,30 +53,46 @@ Larger models trained with more data are on the way.
 # Model Usage
 
 ```python
-from transformers import Wav2Vec2Processor
+from transformers import Wav2Vec2FeatureExtractor
 from transformers import AutoModel
 import torch
 from torch import nn
+import torchaudio.transforms as T
 from datasets import load_dataset
 
+
+# loading our model weights
+model = AutoModel.from_pretrained("m-a-p/MERT-v0", trust_remote_code=True)
+# loading the corresponding preprocessor config
+processor = Wav2Vec2FeatureExtractor.from_pretrained("m-a-p/MERT-v0",trust_remote_code=True)
+
 # load demo audio and set processor
 dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
 dataset = dataset.sort("id")
 sampling_rate = dataset.features["audio"].sampling_rate
-processor = Wav2Vec2Processor.from_pretrained("m-a-p/MERT-v0")
 
-# loading our model weights
-model = AutoModel.from_pretrained("m-a-p/MERT-v0")
+resample_rate = processor.sampling_rate
+# make sure the sample_rate aligned
+if resample_rate != sampling_rate:
+    print(f'setting rate from {sampling_rate} to {resample_rate}')
+    resampler = T.Resample(sampling_rate, resample_rate)
+else:
+    resampler = None
 
 # audio file is decoded on the fly
-inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
+if resampler is None:
+    input_audio = dataset[0]["audio"]["array"]
+else:
+  input_audio = resampler(torch.from_numpy(dataset[0]["audio"]["array"]))
+  
+inputs = processor(input_audio, sampling_rate=resample_rate, return_tensors="pt")
 with torch.no_grad():
     outputs = model(**inputs, output_hidden_states=True)
 
 # take a look at the output shape, there are 13 layers of representation
 # each layer performs differently in different downstream tasks, you should choose empirically
 all_layer_hidden_states = torch.stack(outputs.hidden_states).squeeze()
-print(all_layer_hidden_states.shape) # [13 layer, 292 timestep, 768 feature_dim]
+print(all_layer_hidden_states.shape) # [13 layer, Time steps, 768 feature_dim]
 
 # for utterance level classification tasks, you can simply reduce the representation in time
 time_reduced_hidden_states = all_layer_hidden_states.mean(-2)
@@ -86,6 +102,8 @@ print(time_reduced_hidden_states.shape) # [13, 768]
 aggregator = nn.Conv1d(in_channels=13, out_channels=1, kernel_size=1)
 weighted_avg_hidden_states = aggregator(time_reduced_hidden_states.unsqueeze(0)).squeeze()
 print(weighted_avg_hidden_states.shape) # [768]
+
+
 ```
 
 # Citation