提交 b604d1c7 编写于 作者: L liuyibing01

Merge branch 'develop' into 'master'

examples/deepvoice3 minor fixes

See merge request !73
...@@ -87,7 +87,7 @@ runs/Jul07_09-39-34_instance-mqcyj27y-4/ ...@@ -87,7 +87,7 @@ runs/Jul07_09-39-34_instance-mqcyj27y-4/
... ...
``` ```
Since e use waveflow to synthesize audio while training, so download the trained waveflow model and extract it in current directory before training. Since we use waveflow to synthesize audio while training, so download the trained waveflow model and extract it in current directory before training.
```bash ```bash
wget https://paddlespeech.bj.bcebos.com/Parakeet/waveflow_res128_ljspeech_ckpt_1.0.zip wget https://paddlespeech.bj.bcebos.com/Parakeet/waveflow_res128_ljspeech_ckpt_1.0.zip
......
...@@ -39,6 +39,7 @@ clip_value: 5.0 ...@@ -39,6 +39,7 @@ clip_value: 5.0
clip_norm: 100.0 clip_norm: 100.0
# training: # training:
max_iteration: 1000000
batch_size: 16 batch_size: 16
report_interval: 10000 report_interval: 10000
save_interval: 10000 save_interval: 10000
......
...@@ -62,10 +62,8 @@ class DataCollector(object): ...@@ -62,10 +62,8 @@ class DataCollector(object):
for example in examples: for example in examples:
text, spec, mel, _ = example text, spec, mel, _ = example
text_seqs.append(en.text_to_sequence(text, self.p_pronunciation)) text_seqs.append(en.text_to_sequence(text, self.p_pronunciation))
# if max_frames - mel.shape[0] < 0: specs.append(np.pad(spec, [(0, max_frames - spec.shape[0]), (0, 0)], mode="constant"))
# import pdb; pdb.set_trace() mels.append(np.pad(mel, [(0, max_frames - mel.shape[0]), (0, 0)], mode="constant"))
specs.append(np.pad(spec, [(0, max_frames - spec.shape[0]), (0, 0)]))
mels.append(np.pad(mel, [(0, max_frames - mel.shape[0]), (0, 0)]))
specs = np.stack(specs) specs = np.stack(specs)
mels = np.stack(mels) mels = np.stack(mels)
......
...@@ -81,7 +81,7 @@ def train(args, config): ...@@ -81,7 +81,7 @@ def train(args, config):
optim = create_optimizer(model, config) optim = create_optimizer(model, config)
global global_step global global_step
max_iteration = 1000000 max_iteration = config["max_iteration"]
iterator = iter(tqdm.tqdm(train_loader)) iterator = iter(tqdm.tqdm(train_loader))
while global_step <= max_iteration: while global_step <= max_iteration:
......
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