diff --git a/requirements.txt b/requirements.txt
index bc0455a..a5c1d73 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
 torch>=2
 torchaudio>=2
-faster-whisper>=0.8
+git+https://github.com/SYSTRAN/faster-whisper.git@0.10.0
 transformers
 pandas
 setuptools>=65
diff --git a/whisperx/asr.py b/whisperx/asr.py
index 94e0311..dba8271 100644
--- a/whisperx/asr.py
+++ b/whisperx/asr.py
@@ -140,7 +140,12 @@ class FasterWhisperPipeline(Pipeline):
 
     def preprocess(self, audio):
         audio = audio['inputs']
-        features = log_mel_spectrogram(audio, padding=N_SAMPLES - audio.shape[0])
+        model_n_mels = self.model.feat_kwargs.get("feature_size")
+        features = log_mel_spectrogram(
+            audio,
+            n_mels=model_n_mels if model_n_mels is not None else 80,
+            padding=N_SAMPLES - audio.shape[0],
+        )
         return {'inputs': features}
 
     def _forward(self, model_inputs):
@@ -240,7 +245,9 @@ class FasterWhisperPipeline(Pipeline):
     def detect_language(self, audio: np.ndarray):
         if audio.shape[0] < N_SAMPLES:
             print("Warning: audio is shorter than 30s, language detection may be inaccurate.")
+        model_n_mels = self.model.feat_kwargs.get("feature_size")
         segment = log_mel_spectrogram(audio[: N_SAMPLES],
+                                      n_mels=model_n_mels if model_n_mels is not None else 80,
                                       padding=0 if audio.shape[0] >= N_SAMPLES else N_SAMPLES - audio.shape[0])
         encoder_output = self.model.encode(segment)
         results = self.model.model.detect_language(encoder_output)
diff --git a/whisperx/assets/mel_filters.npz b/whisperx/assets/mel_filters.npz
index 1a78392..28ea269 100644
Binary files a/whisperx/assets/mel_filters.npz and b/whisperx/assets/mel_filters.npz differ
diff --git a/whisperx/audio.py b/whisperx/audio.py
index 48fe3c1..db210fb 100644
--- a/whisperx/audio.py
+++ b/whisperx/audio.py
@@ -12,7 +12,6 @@ from .utils import exact_div
 # hard-coded audio hyperparameters
 SAMPLE_RATE = 16000
 N_FFT = 400
-N_MELS = 80
 HOP_LENGTH = 160
 CHUNK_LENGTH = 30
 N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE  # 480000 samples in a 30-second chunk
@@ -93,7 +92,7 @@ def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
 
 
 @lru_cache(maxsize=None)
-def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
+def mel_filters(device, n_mels: int) -> torch.Tensor:
     """
     load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
     Allows decoupling librosa dependency; saved using:
@@ -103,7 +102,7 @@ def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
             mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
         )
     """
-    assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
+    assert n_mels in [80, 128], f"Unsupported n_mels: {n_mels}"
     with np.load(
         os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")
     ) as f:
@@ -112,7 +111,7 @@ def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
 
 def log_mel_spectrogram(
     audio: Union[str, np.ndarray, torch.Tensor],
-    n_mels: int = N_MELS,
+    n_mels: int,
     padding: int = 0,
     device: Optional[Union[str, torch.device]] = None,
 ):