Upload README.md

README.md

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+---
+license: apache-2.0
+---
+# hubert-base-ch-speech-emotion-recognition
+This model uses [TencentGameMate/chinese-hubert-base]([TencentGameMate/chinese-hubert-base · Hugging Face](https://huggingface.co/TencentGameMate/chinese-hubert-base)) as the pre-training model for training on the CASIA dataset.
+
+The CASIA dataset provides 1200 samples of recordings from actor performing on 6 different emotions in Chinese(The official website provides a total of 9600 pieces of data, and the data set I used may not be complete), which are:
+
+```python
+emotions = ['anger', 'fear', 'happy', 'neutral', 'sad', 'surprise']
+```
+
+# Usage
+```python
+import os
+import random
+
+import librosa
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import AutoConfig, Wav2Vec2FeatureExtractor, HubertPreTrainedModel, HubertModel
+
+model_name_or_path = "xmj2002/hubert-base-ch-speech-emotion-recognition"
+duration = 6
+sample_rate = 16000
+
+config = AutoConfig.from_pretrained(
+    pretrained_model_name_or_path=model_name_or_path,
+)
+
+
+def id2class(id):
+    if id == 0:
+        return "angry"
+    elif id == 1:
+        return "fear"
+    elif id == 2:
+        return "happy"
+    elif id == 3:
+        return "neutral"
+    elif id == 4:
+        return "sad"
+    else:
+        return "surprise"
+
+
+def predict(path, processor, model):
+    speech, sr = librosa.load(path=path, sr=sample_rate)
+    speech = processor(speech, padding="max_length", truncation=True, max_length=duration * sr,
+                       return_tensors="pt", sampling_rate=sr).input_values
+    with torch.no_grad():
+        logit = model(speech)
+    score = F.softmax(logit, dim=1).detach().cpu().numpy()[0]
+    id = torch.argmax(logit).cpu().numpy()
+    print(f"file path: {path} \t predict: {id2class(id)} \t score:{score[id]} ")
+
+
+class HubertClassificationHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.classifier_dropout)
+        self.out_proj = nn.Linear(config.hidden_size, config.num_class)
+
+    def forward(self, x):
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.dropout(x)
+        x = self.out_proj(x)
+        return x
+
+
+class HubertForSpeechClassification(HubertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.hubert = HubertModel(config)
+        self.classifier = HubertClassificationHead(config)
+        self.init_weights()
+
+    def forward(self, x):
+        outputs = self.hubert(x)
+        hidden_states = outputs[0]
+        x = torch.mean(hidden_states, dim=1)
+        x = self.classifier(x)
+        return x
+
+
+processor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)
+model = HubertForSpeechClassification.from_pretrained(
+    model_name_or_path,
+    config=config,
+)
+model.eval()
+
+file_path = [f"test_data/{path}" for path in os.listdir("test_data")]
+path = random.sample(file_path, 1)[0]
+predict(path, processor, model)
+
+```
+# Training setting
+
+* Data set segmentation ratio: training set: verification set: test set = 0.6:0.2:0.2
+
+* seed: 34
+
+* batch_size: 36
+
+* lr: 2e-4
+
+* optimizer: AdamW(betas=(0.93,0.98), weight_decay=0.2)
+
+* scheduler: Step_LR(step_size=10, gamma=0.3)
+
+* classifier dropout: 0.1
+
+* optimizer parameter:
+
+  ```python
+    for name, param in model.named_parameters():
+        if "hubert" in name:
+            parameter.append({'params': param, 'lr': 0.2 * lr})
+        else:
+            parameter.append({'params': param, "lr": lr})
+  
+  ```
+
+  
+
+# Metric
+
+**Loss(test set): 0.1165**
+
+**Accuracy(test set): 0.972**
+
+*Accuracy curve of training set and verification set*
+
+<div> <img src="https://huggingface.co/xmj2002/hubert-base-ch-speech-emotion-recognition/resolve/main/accuracy.png" width = 80%/> </div>
+
+
+*Loss curve of training set and verification set*
+
+<div> <img src="https://huggingface.co/xmj2002/hubert-base-ch-speech-emotion-recognition/resolve/main/loss.png" width = 80%/> </div>