diff --git a/parler_tts/streamer.py b/parler_tts/streamer.py
index 57dab91..df8f386 100644
--- a/parler_tts/streamer.py
+++ b/parler_tts/streamer.py
@@ -1,7 +1,6 @@
-
 from .modeling_parler_tts import ParlerTTSForConditionalGeneration
 from transformers.generation.streamers import BaseStreamer
-from typing import Optional
+from typing import Optional, Generator
 import torch
 import numpy as np
 import math
@@ -58,7 +57,7 @@ def __init__(
         self.stop_signal = None
         self.timeout = timeout
 
-    def apply_delay_pattern_mask(self, input_ids):
+    def apply_delay_pattern_mask(self, input_ids: torch.Tensor) -> np.ndarray:
         # build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to Parler)
         _, delay_pattern_mask = self.decoder.build_delay_pattern_mask(
             input_ids[:, :1],
@@ -97,7 +96,7 @@ def apply_delay_pattern_mask(self, input_ids):
         audio_values = output_values.audio_values[0, 0]
         return audio_values.cpu().float().numpy()
 
-    def put(self, value):
+    def put(self, value: torch.Tensor) -> None:
         batch_size = value.shape[0] // self.decoder.num_codebooks
         if batch_size > 1:
             raise ValueError("ParlerTTSStreamer only supports batch size 1")
@@ -112,7 +111,7 @@ def put(self, value):
             self.on_finalized_audio(audio_values[self.to_yield : -self.stride])
             self.to_yield += len(audio_values) - self.to_yield - self.stride
 
-    def end(self):
+    def end(self) -> None:
         """Flushes any remaining cache and appends the stop symbol."""
         if self.token_cache is not None:
             audio_values = self.apply_delay_pattern_mask(self.token_cache)
@@ -121,18 +120,19 @@ def end(self):
 
         self.on_finalized_audio(audio_values[self.to_yield :], stream_end=True)
 
-    def on_finalized_audio(self, audio: np.ndarray, stream_end: bool = False):
+    def on_finalized_audio(self, audio: np.ndarray, stream_end: bool = False) -> None:
         """Put the new audio in the queue. If the stream is ending, also put a stop signal in the queue."""
         self.audio_queue.put(audio, timeout=self.timeout)
         if stream_end:
             self.audio_queue.put(self.stop_signal, timeout=self.timeout)
 
-    def __iter__(self):
+    def __iter__(self) -> Generator[np.ndarray, None, None]:
         return self
 
-    def __next__(self):
+    def __next__(self) -> np.ndarray:
         value = self.audio_queue.get(timeout=self.timeout)
         if not isinstance(value, np.ndarray) and value == self.stop_signal:
             raise StopIteration()
         else:
-            return value
\ No newline at end of file
+            return value
+