fix lint

cosyvoice/bin/export_jit.py

@@ -19,12 +19,13 @@ import logging
 logging.getLogger('matplotlib').setLevel(logging.WARNING)
 import os
 import sys
+import torch
 ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
 sys.path.append('{}/../..'.format(ROOT_DIR))
 sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR))
-import torch
 from cosyvoice.cli.cosyvoice import CosyVoice
 
+
 def get_args():
     parser = argparse.ArgumentParser(description='export your model for deployment')
     parser.add_argument('--model_dir',
@@ -35,6 +36,7 @@ def get_args():
     print(args)
     return args
 
+
 def main():
     args = get_args()
     logging.basicConfig(level=logging.DEBUG,
@@ -67,5 +69,6 @@ def main():
     script = torch.jit.optimize_for_inference(script)
     script.save('{}/flow.encoder.fp32.zip'.format(args.model_dir))
 
+
 if __name__ == '__main__':
     main()

cosyvoice/bin/export_onnx.py

@@ -20,13 +20,13 @@ import logging
 logging.getLogger('matplotlib').setLevel(logging.WARNING)
 import os
 import sys
-ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
-sys.path.append('{}/../..'.format(ROOT_DIR))
-sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR))
 import onnxruntime
 import random
 import torch
 from tqdm import tqdm
+ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append('{}/../..'.format(ROOT_DIR))
+sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR))
 from cosyvoice.cli.cosyvoice import CosyVoice
 
 
@@ -50,6 +50,7 @@ def get_args():
     print(args)
     return args
 
+
 def main():
     args = get_args()
     logging.basicConfig(level=logging.DEBUG,
@@ -89,7 +90,8 @@ def main():
     option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
     option.intra_op_num_threads = 1
     providers = ['CUDAExecutionProvider' if torch.cuda.is_available() else 'CPUExecutionProvider']
-    estimator_onnx = onnxruntime.InferenceSession('{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir), sess_options=option, providers=providers)
+    estimator_onnx = onnxruntime.InferenceSession('{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
+                                                  sess_options=option, providers=providers)
 
     for _ in tqdm(range(10)):
         x, mask, mu, t, spks, cond = get_dummy_input(random.randint(1, 6), random.randint(16, 512), out_channels, device)
@@ -105,5 +107,6 @@ def main():
         output_onnx = estimator_onnx.run(None, ort_inputs)[0]
         torch.testing.assert_allclose(output_pytorch, torch.from_numpy(output_onnx).to(device), rtol=1e-2, atol=1e-4)
 
+
 if __name__ == "__main__":
     main()

cosyvoice/bin/inference.py

@@ -18,16 +18,15 @@ import argparse
 import logging
 logging.getLogger('matplotlib').setLevel(logging.WARNING)
 import os
-
 import torch
 from torch.utils.data import DataLoader
 import torchaudio
 from hyperpyyaml import load_hyperpyyaml
 from tqdm import tqdm
 from cosyvoice.cli.model import CosyVoiceModel
-
 from cosyvoice.dataset.dataset import Dataset
 
+
 def get_args():
     parser = argparse.ArgumentParser(description='inference with your model')
     parser.add_argument('--config', required=True, help='config file')
@@ -66,7 +65,8 @@ def main():
     model = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift'])
     model.load(args.llm_model, args.flow_model, args.hifigan_model)
 
-    test_dataset = Dataset(args.prompt_data, data_pipeline=configs['data_pipeline'], mode='inference', shuffle=False, partition=False, tts_file=args.tts_text, prompt_utt2data=args.prompt_utt2data)
+    test_dataset = Dataset(args.prompt_data, data_pipeline=configs['data_pipeline'], mode='inference', shuffle=False, partition=False,
+                           tts_file=args.tts_text, prompt_utt2data=args.prompt_utt2data)
     test_data_loader = DataLoader(test_dataset, batch_size=None, num_workers=0)
 
     del configs
@@ -74,13 +74,11 @@ def main():
     fn = os.path.join(args.result_dir, 'wav.scp')
     f = open(fn, 'w')
     with torch.no_grad():
-        for batch_idx, batch in tqdm(enumerate(test_data_loader)):
+        for _, batch in tqdm(enumerate(test_data_loader)):
             utts = batch["utts"]
             assert len(utts) == 1, "inference mode only support batchsize 1"
-            text = batch["text"]
             text_token = batch["text_token"].to(device)
             text_token_len = batch["text_token_len"].to(device)
-            tts_text = batch["tts_text"]
             tts_index = batch["tts_index"]
             tts_text_token = batch["tts_text_token"].to(device)
             tts_text_token_len = batch["tts_text_token_len"].to(device)

cosyvoice/bin/train.py

@@ -132,5 +132,6 @@ def main():
         executor.train_one_epoc(model, optimizer, scheduler, train_data_loader, cv_data_loader, writer, info_dict, group_join)
         dist.destroy_process_group(group_join)
 
+
 if __name__ == '__main__':
     main()