from typing import Tuple
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import torch
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import torch.nn as nn
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import whisper
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from whisper.audio import HOP_LENGTH, N_FFT, N_SAMPLES
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from funasr.register import tables
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from torch.nn.utils.rnn import pad_sequence
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@tables.register("frontend_classes", "WhisperFrontend")
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class WhisperFrontend(nn.Module):
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"""Speech Representation Using Encoder Outputs from OpenAI's Whisper Model:
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URL: https://github.com/openai/whisper
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"""
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def __init__(
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self,
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fs: int = 16000,
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whisper_model: str = None,
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do_pad_trim: bool = True,
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n_mels: int = 80,
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):
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super().__init__()
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assert fs == 16000
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self.fs = fs
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self.n_fft = N_FFT
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self.win_length = N_FFT
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self.hop_length = HOP_LENGTH
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self.pad_samples = N_SAMPLES
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self.frame_shift = self.hop_length
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self.lfr_n = 1
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self.n_mels = n_mels
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if whisper_model == "large-v3" or whisper_model == "large":
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self.n_mels = 128
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self.mel_filters = whisper.audio.mel_filters
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self.do_pad_trim = do_pad_trim
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if do_pad_trim:
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self.pad_or_trim = whisper.pad_or_trim
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# assert whisper_model in whisper.available_models()
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def output_size(self) -> int:
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return self.n_mels
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def log_mel_spectrogram(
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self,
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audio: torch.Tensor,
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ilens: torch.Tensor = None,
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) -> torch.Tensor:
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window = torch.hann_window(self.win_length).to(audio.device)
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stft = torch.stft(
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audio, self.n_fft, self.hop_length, window=window, return_complex=True
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)
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# whisper deletes the last frame by default (Shih-Lun)
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magnitudes = stft[..., :-1].abs() ** 2
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filters = self.mel_filters(audio.device, self.n_mels)
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mel_spec = filters @ magnitudes
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log_spec = torch.clamp(mel_spec, min=1e-10).log10()
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if ilens is not None:
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olens = ilens // self.hop_length
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else:
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olens = None
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log_spec = torch.maximum(
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log_spec,
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log_spec.view(audio.size(0), -1).max(dim=-1)[0][:, None, None] - 8.0,
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)
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log_spec = (log_spec + 4.0) / 4.0
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return log_spec, olens
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def forward(
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self, input: torch.Tensor, input_lengths: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor]:
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batch_size = input.size(0)
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feats = []
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feats_lens = []
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for i in range(batch_size):
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if self.do_pad_trim:
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feat = self.pad_or_trim(input[i], self.pad_samples)
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else:
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feat = input[i]
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feat, feat_len = self.log_mel_spectrogram(feat[None, :], input_lengths[0])
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feats.append(feat[0])
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feats_lens.append(feat_len)
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feats_lens = torch.as_tensor(feats_lens)
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if batch_size == 1:
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feats_pad = feats[0][None, :, :]
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else:
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feats_pad = pad_sequence(feats,
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batch_first=True,
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padding_value=0.0)
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return feats_pad, feats_lens
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