fix lint

examples/grpo/cosyvoice2/token2wav_asr_server.py

@@ -14,6 +14,13 @@
 # limitations under the License.
 """Pytriton server for token2wav conversion and ASR"""
 
+from datasets import load_dataset
+from cosyvoice.cli.cosyvoice import CosyVoice2
+from omnisense.models import OmniSenseVoiceSmall
+from pytriton.proxy.types import Request
+from pytriton.triton import Triton, TritonConfig
+from pytriton.model_config import DynamicBatcher, ModelConfig, Tensor
+from pytriton.decorators import batch
 import argparse
 import io
 import logging
@@ -37,15 +44,6 @@ zh_tn_model = ZhNormalizer(
     overwrite_cache=True,
 )
 
-from pytriton.decorators import batch
-from pytriton.model_config import DynamicBatcher, ModelConfig, Tensor
-from pytriton.triton import Triton, TritonConfig
-from pytriton.proxy.types import Request
-
-from omnisense.models import OmniSenseVoiceSmall
-from cosyvoice.cli.cosyvoice import CosyVoice2
-
-from datasets import load_dataset
 
 sys.path.append("/workspace/CosyVoice/third_party/Matcha-TTS")
 
@@ -78,7 +76,6 @@ class _ASR_Server:
         return {"TRANSCRIPTS": transcripts}
 
 
-
 def audio_decode_cosyvoice2(
     audio_tokens, prompt_text, prompt_speech_16k, codec_decoder
 ):
@@ -123,12 +120,12 @@ def get_random_prompt_from_dataset(dataset):
     """
     random_idx = random.randint(0, len(dataset) - 1)
     sample = dataset[random_idx]
-    
+
     # Extract audio data
     audio_data = sample["audio"]
     audio_array = audio_data["array"]
     sample_rate = audio_data["sampling_rate"]
-    
+
     # Convert audio to 16kHz if needed
     if sample_rate != 16000:
         num_samples = int(len(audio_array) * (16000 / sample_rate))
@@ -141,6 +138,7 @@ def get_random_prompt_from_dataset(dataset):
     prompt_text = prompt_text.replace(" ", "")
     return prompt_text, prompt_speech_16k
 
+
 class _Token2Wav_ASR:
     """Wraps a single OmniSenseVoiceSmall model instance for Triton."""
 
@@ -163,6 +161,7 @@ class _Token2Wav_ASR:
             self.codec_decoder = CosyVoice2(
                 "/workspace/CosyVoice2-0.5B", load_jit=True, load_trt=True, fp16=True
             )
+
     @batch
     def __call__(self, TOKENS: np.ndarray, TOKEN_LENS: np.ndarray, GT_TEXT: np.ndarray):
         """
@@ -236,7 +235,6 @@ class _Token2Wav_ASR:
         transcripts = np.char.encode(np.array(texts).reshape(-1, 1), "utf-8")
         rewards_arr = np.array(rewards, dtype=np.float32).reshape(-1, 1)
 
-
         return {"REWARDS": rewards_arr, "TRANSCRIPTS": transcripts}