clean code

s2s_pipeline.py

@@ -13,6 +13,7 @@ from time import perf_counter
 
 import numpy as np
 import torch
+import nltk
 from nltk.tokenize import sent_tokenize
 from rich.console import Console
 from transformers import (
@@ -36,6 +37,11 @@ from utils import (
     next_power_of_2
 )
 
+# Ensure that the necessary NLTK resources are available
+try:
+    nltk.data.find('tokenizers/punkt_tab')
+except LookupError:
+    nltk.download('punkt_tab')
 
 # caching allows ~50% compilation time reduction
 # see https://docs.google.com/document/d/1y5CRfMLdwEoF1nTk9q8qEu1mgMUuUtvhklPKJ2emLU8/edit#heading=h.o2asbxsrp1ma
@@ -337,9 +343,9 @@ class VADHandler(BaseHandler):
             array = torch.cat(vad_output).cpu().numpy()
             duration_ms = len(array) / self.sample_rate * 1000
             if duration_ms < self.min_speech_ms or duration_ms > self.max_speech_ms:
-                logger.debug(f"audio input of duration: {len(array) / self._sample_rate}s, skipping")
+                logger.debug(f"audio input of duration: {len(array) / self.sample_rate}s, skipping")
             else:
-                self._should_listen.clear()
+                self.should_listen.clear()
                 logger.debug("Stop listening")
                 yield array