diff --git a/assets/mel_filters.npz b/assets/mel_filters.npz
new file mode 100644
index 0000000..28ea269
Binary files /dev/null and b/assets/mel_filters.npz differ
diff --git a/requirements.txt b/requirements.txt
index 4f6317d..d2416b6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -13,6 +13,5 @@ transformers>=4.41.2,<=4.49.0,!=4.46.*,!=4.47.*,!=4.48.*;python_version<'3.10'
 transformers>=4.41.2,<=4.49.0,!=4.46.*,!=4.47.*,!=4.48.0;python_version>='3.10'
 x-transformers==1.44.4
 torchdiffeq==0.2.5
-openai-whisper==20240930
 httpx==0.28.1
 gradio==5.23.1
diff --git a/tts/frontend_function.py b/tts/frontend_function.py
index d6236de..f9c1f1a 100644
--- a/tts/frontend_function.py
+++ b/tts/frontend_function.py
@@ -12,13 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
 import torch
 import torch.nn.functional as F
-import whisper
 import librosa
 from copy import deepcopy
 from tts.utils.text_utils.ph_tone_convert import split_ph_timestamp, split_ph
 from tts.utils.audio_utils.align import mel2token_to_dur
+from subprocess import CalledProcessError, run
+import numpy as np
 
 ''' Graphme to phoneme function '''
 def g2p(self, text_inp):
@@ -40,7 +42,7 @@ def g2p(self, text_inp):
 def align(self, wav):
     with torch.inference_mode():
         whisper_wav = librosa.resample(wav, orig_sr=self.sr, target_sr=16000)
-        mel = torch.FloatTensor(whisper.log_mel_spectrogram(whisper_wav).T).to(self.device)[None].transpose(1,2)
+        mel = torch.FloatTensor(log_mel_spectrogram(whisper_wav).T).to(self.device)[None].transpose(1,2)
         prompt_max_frame = mel.size(2) // self.fm * self.fm
         mel = mel[:, :, :prompt_max_frame]
         token = torch.LongTensor([[798]]).to(self.device)
@@ -179,3 +181,113 @@ def prepare_inputs_for_dit(self, mel2ph_ref, mel2ph_pred, ph_ref, tone_ref, ph_p
         "ctx_mask": ctx_mask,
         "dur": mel2ph_pred,
     }
+
+
+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:
+
+        np.savez_compressed(
+            "mel_filters.npz",
+            mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
+            mel_128=librosa.filters.mel(sr=16000, n_fft=400, n_mels=128),
+        )
+    """
+    assert n_mels in {80, 128}, f"Unsupported n_mels: {n_mels}"
+
+    filters_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "assets", "mel_filters.npz")
+    with np.load(filters_path, allow_pickle=False) as f:
+        return torch.from_numpy(f[f"mel_{n_mels}"]).to(device)
+
+SAMPLE_RATE = 16000
+N_FFT = 400
+HOP_LENGTH = 160
+
+def load_audio(file: str, sr: int = SAMPLE_RATE):
+    """
+    Open an audio file and read as mono waveform, resampling as necessary
+
+    Parameters
+    ----------
+    file: str
+        The audio file to open
+
+    sr: int
+        The sample rate to resample the audio if necessary
+
+    Returns
+    -------
+    A NumPy array containing the audio waveform, in float32 dtype.
+    """
+
+    # This launches a subprocess to decode audio while down-mixing
+    # and resampling as necessary.  Requires the ffmpeg CLI in PATH.
+    # fmt: off
+    cmd = [
+        "ffmpeg",
+        "-nostdin",
+        "-threads", "0",
+        "-i", file,
+        "-f", "s16le",
+        "-ac", "1",
+        "-acodec", "pcm_s16le",
+        "-ar", str(sr),
+        "-"
+    ]
+    # fmt: on
+    try:
+        out = run(cmd, capture_output=True, check=True).stdout
+    except CalledProcessError as e:
+        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
+
+    return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
+
+def log_mel_spectrogram(
+    audio,
+    n_mels: int = 80,
+    padding: int = 0,
+    device = None,
+):
+    """
+    Compute the log-Mel spectrogram of
+
+    Parameters
+    ----------
+    audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
+        The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
+
+    n_mels: int
+        The number of Mel-frequency filters, only 80 and 128 are supported
+
+    padding: int
+        Number of zero samples to pad to the right
+
+    device: Optional[Union[str, torch.device]]
+        If given, the audio tensor is moved to this device before STFT
+
+    Returns
+    -------
+    torch.Tensor, shape = (n_mels, n_frames)
+        A Tensor that contains the Mel spectrogram
+    """
+    if not torch.is_tensor(audio):
+        if isinstance(audio, str):
+            audio = load_audio(audio)
+        audio = torch.from_numpy(audio)
+
+    if device is not None:
+        audio = audio.to(device)
+    if padding > 0:
+        audio = F.pad(audio, (0, padding))
+    window = torch.hann_window(N_FFT).to(audio.device)
+    stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
+    magnitudes = stft[..., :-1].abs() ** 2
+
+    filters = mel_filters(audio.device, n_mels)
+    mel_spec = filters @ magnitudes
+
+    log_spec = torch.clamp(mel_spec, min=1e-10).log10()
+    log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
+    log_spec = (log_spec + 4.0) / 4.0
+    return log_spec
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