freeze wav2vec2=True, change loss report and update README.md

examples/librispeech/asr3/README.md

@@ -88,6 +88,12 @@ data/
 |-- test.meta
 `-- train.meta
 ```
+
+Stage 0 also downloads the pre-trained [wav2vec2](https://paddlespeech.bj.bcebos.com/wav2vec/wav2vec2-large-960h-lv60-self.pdparams) model.
+```bash
+mkdir -p exp/wav2vec2
+wget -P exp/wav2vec2 https://paddlespeech.bj.bcebos.com/wav2vec/wav2vec2-large-960h-lv60-self.pdparams
+```
 ## Stage 1: Model Training
 If you want to train the model. you can use stage 1 in `run.sh`. The code is shown below. 
 ```bash

examples/librispeech/asr3/conf/wav2vec2ASR.yaml

 ############################################
 #          Network Architecture           #
 ############################################
-freeze_wav2vec2: False
+freeze_wav2vec2: True
 normalize_wav: True
 output_norm: True
 dnn_blocks: 2

paddlespeech/s2t/exps/wav2vec2/model.py

@@ -48,6 +48,24 @@ class Wav2Vec2ASRTrainer(Trainer):
         super().__init__(config, args)
         self.avg_train_loss = 0
 
+    def update_average(self, batch_index, loss, avg_loss):
+        """Update running average of the loss.
+        Arguments
+        ---------
+        loss : paddle.tensor
+            detached loss, a single float value.
+        avg_loss : float
+            current running average.
+        Returns
+        -------
+        avg_loss : float
+            The average loss.
+        """
+        if paddle.isfinite(loss):
+            avg_loss -= avg_loss / (batch_index + 1)
+            avg_loss += float(loss) / (batch_index + 1)
+        return avg_loss
+
     def train_batch(self, batch_index, batch, msg):
         train_conf = self.config
         start = time.time()
@@ -59,11 +77,11 @@ class Wav2Vec2ASRTrainer(Trainer):
         wav = wav[:, :, 0]
         wav = self.speech_augmentation(wav, wavs_lens_rate)
         loss = self.model(wav, wavs_lens_rate, target, target_lens_rate)
-        # pring(wav, wavs_lens_rate, target, target_lens_rate)
         # loss div by `batch_size * accum_grad`
         loss /= train_conf.accum_grad
 
-        losses_np = {'loss': float(loss) * train_conf.accum_grad}
+        self.avg_train_loss = self.update_average(batch_index, loss,
+                                                  self.avg_train_loss)
 
         # loss backward
         if (batch_index + 1) % train_conf.accum_grad != 0:
@@ -87,6 +105,8 @@ class Wav2Vec2ASRTrainer(Trainer):
             self.optimizer.clear_grad()
             self.lr_scheduler.step()
             self.iteration += 1
+
+        losses_np = {'loss': float(self.avg_train_loss) * train_conf.accum_grad}
         iteration_time = time.time() - start
         for k, v in losses_np.items():
             report(k, v)