makaveli10
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# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import os
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from collections import defaultdict
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from functools import lru_cache
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from pathlib import Path
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from subprocess import CalledProcessError, run
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from typing import Dict, Iterable, List, Optional, TextIO, Tuple, Union
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import kaldialign
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import numpy as np
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import soundfile
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import av
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import wave
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import torch
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import torch.nn.functional as F
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from whisper_live.utils import resample
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Pathlike = Union[str, Path]
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SAMPLE_RATE = 16000
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N_FFT = 400
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HOP_LENGTH = 160
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CHUNK_LENGTH = 30
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N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000 samples in a 30-second chunk
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def load_audio(file: str, sr: int = 16000):
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"""
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Open an audio file, resample it, and read as a mono waveform.
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Parameters
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----------
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file: str
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The audio file to open.
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sr: int
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The sample rate to resample the audio if necessary.
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Returns
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-------
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A NumPy array containing the audio waveform, in float32 dtype.
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"""
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resampled_file = resample(file, sr)
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with wave.open(resampled_file, "rb") as wav_file:
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num_frames = wav_file.getnframes()
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raw_data = wav_file.readframes(num_frames)
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audio_data = np.frombuffer(raw_data, dtype=np.int16)
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audio_data = audio_data.astype(np.float32) / 32768.0
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return audio_data
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def load_audio_wav_format(wav_path):
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# make sure audio in .wav format
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assert wav_path.endswith(
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'.wav'), f"Only support .wav format, but got {wav_path}"
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waveform, sample_rate = soundfile.read(wav_path)
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assert sample_rate == 16000, f"Only support 16k sample rate, but got {sample_rate}"
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return waveform, sample_rate
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def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
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"""
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Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
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"""
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if torch.is_tensor(array):
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if array.shape[axis] > length:
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array = array.index_select(dim=axis,
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index=torch.arange(length,
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device=array.device))
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if array.shape[axis] < length:
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pad_widths = [(0, 0)] * array.ndim
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pad_widths[axis] = (0, length - array.shape[axis])
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array = F.pad(array,
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[pad for sizes in pad_widths[::-1] for pad in sizes])
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else:
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if array.shape[axis] > length:
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array = array.take(indices=range(length), axis=axis)
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if array.shape[axis] < length:
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pad_widths = [(0, 0)] * array.ndim
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pad_widths[axis] = (0, length - array.shape[axis])
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array = np.pad(array, pad_widths)
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return array
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@lru_cache(maxsize=None)
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def mel_filters(device,
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n_mels: int,
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mel_filters_dir: str = None) -> torch.Tensor:
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"""
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load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
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Allows decoupling librosa dependency; saved using:
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np.savez_compressed(
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"mel_filters.npz",
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mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
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)
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"""
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assert n_mels in {80, 128}, f"Unsupported n_mels: {n_mels}"
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if mel_filters_dir is None:
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mel_filters_path = os.path.join(os.path.dirname(__file__), "assets",
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"mel_filters.npz")
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else:
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mel_filters_path = os.path.join(mel_filters_dir, "mel_filters.npz")
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with np.load(mel_filters_path) as f:
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return torch.from_numpy(f[f"mel_{n_mels}"]).to(device)
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def log_mel_spectrogram(
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audio: Union[str, np.ndarray, torch.Tensor],
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n_mels: int,
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padding: int = 0,
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device: Optional[Union[str, torch.device]] = None,
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return_duration: bool = False,
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mel_filters_dir: str = None,
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):
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"""
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Compute the log-Mel spectrogram of
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Parameters
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----------
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audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
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The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
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n_mels: int
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The number of Mel-frequency filters, only 80 and 128 are supported
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padding: int
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Number of zero samples to pad to the right
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device: Optional[Union[str, torch.device]]
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If given, the audio tensor is moved to this device before STFT
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Returns
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-------
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torch.Tensor, shape = (80 or 128, n_frames)
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A Tensor that contains the Mel spectrogram
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"""
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if not torch.is_tensor(audio):
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if isinstance(audio, str):
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if audio.endswith('.wav'):
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audio, _ = load_audio_wav_format(audio)
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else:
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audio = load_audio(audio)
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assert isinstance(audio,
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np.ndarray), f"Unsupported audio type: {type(audio)}"
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duration = audio.shape[-1] / SAMPLE_RATE
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audio = pad_or_trim(audio, N_SAMPLES)
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audio = audio.astype(np.float32)
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audio = torch.from_numpy(audio)
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if device is not None:
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audio = audio.to(device)
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if padding > 0:
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audio = F.pad(audio, (0, padding))
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window = torch.hann_window(N_FFT).to(audio.device)
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stft = torch.stft(audio,
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N_FFT,
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HOP_LENGTH,
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window=window,
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return_complex=True)
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magnitudes = stft[..., :-1].abs()**2
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filters = mel_filters(audio.device, n_mels, mel_filters_dir)
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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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log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
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log_spec = (log_spec + 4.0) / 4.0
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if return_duration:
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return log_spec, duration
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else:
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return log_spec
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def store_transcripts(filename: Pathlike, texts: Iterable[Tuple[str, str,
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str]]) -> None:
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"""Save predicted results and reference transcripts to a file.
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https://github.com/k2-fsa/icefall/blob/master/icefall/utils.py
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Args:
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filename:
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File to save the results to.
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texts:
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An iterable of tuples. The first element is the cur_id, the second is
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the reference transcript and the third element is the predicted result.
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Returns:
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Return None.
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"""
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with open(filename, "w") as f:
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for cut_id, ref, hyp in texts:
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print(f"{cut_id}:\tref={ref}", file=f)
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print(f"{cut_id}:\thyp={hyp}", file=f)
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def write_error_stats( # noqa: C901
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f: TextIO,
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test_set_name: str,
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results: List[Tuple[str, str]],
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enable_log: bool = True,
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) -> float:
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"""Write statistics based on predicted results and reference transcripts.
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https://github.com/k2-fsa/icefall/blob/master/icefall/utils.py
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It will write the following to the given file:
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- WER
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- number of insertions, deletions, substitutions, corrects and total
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reference words. For example::
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Errors: 23 insertions, 57 deletions, 212 substitutions, over 2606
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reference words (2337 correct)
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- The difference between the reference transcript and predicted result.
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An instance is given below::
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THE ASSOCIATION OF (EDISON->ADDISON) ILLUMINATING COMPANIES
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The above example shows that the reference word is `EDISON`,
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but it is predicted to `ADDISON` (a substitution error).
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Another example is::
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FOR THE FIRST DAY (SIR->*) I THINK
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The reference word `SIR` is missing in the predicted
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results (a deletion error).
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results:
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An iterable of tuples. The first element is the cur_id, the second is
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the reference transcript and the third element is the predicted result.
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enable_log:
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If True, also print detailed WER to the console.
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Otherwise, it is written only to the given file.
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Returns:
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Return None.
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"""
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subs: Dict[Tuple[str, str], int] = defaultdict(int)
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ins: Dict[str, int] = defaultdict(int)
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dels: Dict[str, int] = defaultdict(int)
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# `words` stores counts per word, as follows:
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# corr, ref_sub, hyp_sub, ins, dels
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words: Dict[str, List[int]] = defaultdict(lambda: [0, 0, 0, 0, 0])
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num_corr = 0
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ERR = "*"
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for cut_id, ref, hyp in results:
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ali = kaldialign.align(ref, hyp, ERR)
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for ref_word, hyp_word in ali:
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if ref_word == ERR:
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ins[hyp_word] += 1
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words[hyp_word][3] += 1
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elif hyp_word == ERR:
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dels[ref_word] += 1
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words[ref_word][4] += 1
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elif hyp_word != ref_word:
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subs[(ref_word, hyp_word)] += 1
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words[ref_word][1] += 1
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words[hyp_word][2] += 1
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else:
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words[ref_word][0] += 1
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num_corr += 1
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ref_len = sum([len(r) for _, r, _ in results])
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sub_errs = sum(subs.values())
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ins_errs = sum(ins.values())
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del_errs = sum(dels.values())
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tot_errs = sub_errs + ins_errs + del_errs
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tot_err_rate = "%.2f" % (100.0 * tot_errs / ref_len)
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if enable_log:
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logging.info(f"[{test_set_name}] %WER {tot_errs / ref_len:.2%} "
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f"[{tot_errs} / {ref_len}, {ins_errs} ins, "
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f"{del_errs} del, {sub_errs} sub ]")
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print(f"%WER = {tot_err_rate}", file=f)
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print(
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f"Errors: {ins_errs} insertions, {del_errs} deletions, "
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f"{sub_errs} substitutions, over {ref_len} reference "
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f"words ({num_corr} correct)",
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file=f,
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)
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print(
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"Search below for sections starting with PER-UTT DETAILS:, "
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"SUBSTITUTIONS:, DELETIONS:, INSERTIONS:, PER-WORD STATS:",
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file=f,
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)
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print("", file=f)
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print("PER-UTT DETAILS: corr or (ref->hyp) ", file=f)
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for cut_id, ref, hyp in results:
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ali = kaldialign.align(ref, hyp, ERR)
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combine_successive_errors = True
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if combine_successive_errors:
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ali = [[[x], [y]] for x, y in ali]
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for i in range(len(ali) - 1):
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if ali[i][0] != ali[i][1] and ali[i + 1][0] != ali[i + 1][1]:
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ali[i + 1][0] = ali[i][0] + ali[i + 1][0]
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ali[i + 1][1] = ali[i][1] + ali[i + 1][1]
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ali[i] = [[], []]
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ali = [[
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list(filter(lambda a: a != ERR, x)),
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list(filter(lambda a: a != ERR, y)),
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] for x, y in ali]
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ali = list(filter(lambda x: x != [[], []], ali))
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ali = [[
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ERR if x == [] else " ".join(x),
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ERR if y == [] else " ".join(y),
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] for x, y in ali]
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print(
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f"{cut_id}:\t" + " ".join((ref_word if ref_word == hyp_word else
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f"({ref_word}->{hyp_word})"
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for ref_word, hyp_word in ali)),
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file=f,
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)
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print("", file=f)
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print("SUBSTITUTIONS: count ref -> hyp", file=f)
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for count, (ref, hyp) in sorted([(v, k) for k, v in subs.items()],
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reverse=True):
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print(f"{count} {ref} -> {hyp}", file=f)
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print("", file=f)
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print("DELETIONS: count ref", file=f)
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for count, ref in sorted([(v, k) for k, v in dels.items()], reverse=True):
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print(f"{count} {ref}", file=f)
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print("", file=f)
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print("INSERTIONS: count hyp", file=f)
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for count, hyp in sorted([(v, k) for k, v in ins.items()], reverse=True):
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print(f"{count} {hyp}", file=f)
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print("", file=f)
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print("PER-WORD STATS: word corr tot_errs count_in_ref count_in_hyp",
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file=f)
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for _, word, counts in sorted([(sum(v[1:]), k, v)
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for k, v in words.items()],
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reverse=True):
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(corr, ref_sub, hyp_sub, ins, dels) = counts
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tot_errs = ref_sub + hyp_sub + ins + dels
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ref_count = corr + ref_sub + dels
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hyp_count = corr + hyp_sub + ins
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print(f"{word} {corr} {tot_errs} {ref_count} {hyp_count}", file=f)
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return float(tot_err_rate)
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File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,426 @@
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import json
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import re
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import math
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from collections import OrderedDict
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from pathlib import Path
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from typing import Union
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import torch
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import numpy as np
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import torch.nn.functional as F
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from whisper.tokenizer import get_tokenizer
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from whisper_live.transcriber.tensorrt_utils import (
|
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mel_filters,
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load_audio_wav_format,
|
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pad_or_trim,
|
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load_audio
|
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)
|
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|
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import tensorrt_llm
|
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import tensorrt_llm.logger as logger
|
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from tensorrt_llm._utils import (str_dtype_to_torch, str_dtype_to_trt,
|
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trt_dtype_to_torch)
|
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from tensorrt_llm.bindings import GptJsonConfig, KVCacheType
|
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from tensorrt_llm.runtime import PYTHON_BINDINGS, ModelConfig, SamplingConfig
|
||||
from tensorrt_llm.runtime.session import Session, TensorInfo
|
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|
||||
|
||||
SAMPLE_RATE = 16000
|
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N_FFT = 400
|
||||
HOP_LENGTH = 160
|
||||
CHUNK_LENGTH = 30
|
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N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000 samples in a 30-second chunk
|
||||
|
||||
def read_config(component, engine_dir):
|
||||
config_path = engine_dir / component / 'config.json'
|
||||
with open(config_path, 'r') as f:
|
||||
config = json.load(f)
|
||||
model_config = OrderedDict()
|
||||
model_config.update(config['pretrained_config'])
|
||||
model_config.update(config['build_config'])
|
||||
return model_config
|
||||
|
||||
|
||||
def remove_tensor_padding(input_tensor,
|
||||
input_tensor_lengths=None,
|
||||
pad_value=None):
|
||||
if pad_value:
|
||||
assert input_tensor_lengths is None, "input_tensor_lengths should be None when pad_value is provided"
|
||||
# Text tensor case: batch, seq_len
|
||||
assert torch.all(
|
||||
input_tensor[:, 0] != pad_value
|
||||
), "First token in each sequence should not be pad_value"
|
||||
assert input_tensor_lengths is None
|
||||
|
||||
# Create a mask for all non-pad tokens
|
||||
mask = input_tensor != pad_value
|
||||
|
||||
# Apply the mask to input_tensor to remove pad tokens
|
||||
output_tensor = input_tensor[mask].view(1, -1)
|
||||
|
||||
else:
|
||||
# Audio tensor case: batch, seq_len, feature_len
|
||||
# position_ids case: batch, seq_len
|
||||
assert input_tensor_lengths is not None, "input_tensor_lengths must be provided for 3D input_tensor"
|
||||
|
||||
# Initialize a list to collect valid sequences
|
||||
valid_sequences = []
|
||||
|
||||
for i in range(input_tensor.shape[0]):
|
||||
valid_length = input_tensor_lengths[i]
|
||||
valid_sequences.append(input_tensor[i, :valid_length])
|
||||
|
||||
# Concatenate all valid sequences along the batch dimension
|
||||
output_tensor = torch.cat(valid_sequences, dim=0)
|
||||
return output_tensor
|
||||
|
||||
|
||||
class WhisperEncoding:
|
||||
|
||||
def __init__(self, engine_dir):
|
||||
self.session = self.get_session(engine_dir)
|
||||
config = read_config('encoder', engine_dir)
|
||||
self.n_mels = config['n_mels']
|
||||
self.dtype = config['dtype']
|
||||
self.num_languages = config['num_languages']
|
||||
self.encoder_config = config
|
||||
|
||||
def get_session(self, engine_dir):
|
||||
serialize_path = engine_dir / 'encoder' / 'rank0.engine'
|
||||
with open(serialize_path, 'rb') as f:
|
||||
session = Session.from_serialized_engine(f.read())
|
||||
return session
|
||||
|
||||
def get_audio_features(self,
|
||||
mel,
|
||||
mel_input_lengths,
|
||||
encoder_downsampling_factor=2):
|
||||
if isinstance(mel, list):
|
||||
longest_mel = max([f.shape[-1] for f in mel])
|
||||
mel = [
|
||||
torch.nn.functional.pad(f, (0, longest_mel - f.shape[-1]),
|
||||
mode='constant') for f in mel
|
||||
]
|
||||
mel = torch.cat(mel, dim=0).type(
|
||||
str_dtype_to_torch("float16")).contiguous()
|
||||
bsz, seq_len = mel.shape[0], mel.shape[2]
|
||||
position_ids = torch.arange(
|
||||
math.ceil(seq_len / encoder_downsampling_factor),
|
||||
dtype=torch.int32,
|
||||
device=mel.device).expand(bsz, -1).contiguous()
|
||||
if self.encoder_config['plugin_config']['remove_input_padding']:
|
||||
# mel B,D,T -> B,T,D -> BxT, D
|
||||
mel = mel.transpose(1, 2)
|
||||
mel = remove_tensor_padding(mel, mel_input_lengths)
|
||||
position_ids = remove_tensor_padding(
|
||||
position_ids, mel_input_lengths // encoder_downsampling_factor)
|
||||
inputs = OrderedDict()
|
||||
inputs['input_features'] = mel
|
||||
inputs['input_lengths'] = mel_input_lengths
|
||||
inputs['position_ids'] = position_ids
|
||||
|
||||
output_list = [
|
||||
TensorInfo('input_features', str_dtype_to_trt(self.dtype),
|
||||
mel.shape),
|
||||
TensorInfo('input_lengths', str_dtype_to_trt('int32'),
|
||||
mel_input_lengths.shape),
|
||||
TensorInfo('position_ids', str_dtype_to_trt('int32'),
|
||||
inputs['position_ids'].shape)
|
||||
]
|
||||
|
||||
output_info = (self.session).infer_shapes(output_list)
|
||||
|
||||
logger.debug(f'output info {output_info}')
|
||||
outputs = {
|
||||
t.name: torch.empty(tuple(t.shape),
|
||||
dtype=trt_dtype_to_torch(t.dtype),
|
||||
device='cuda')
|
||||
for t in output_info
|
||||
}
|
||||
stream = torch.cuda.current_stream()
|
||||
ok = self.session.run(inputs=inputs,
|
||||
outputs=outputs,
|
||||
stream=stream.cuda_stream)
|
||||
assert ok, 'Engine execution failed'
|
||||
stream.synchronize()
|
||||
encoder_output = outputs['encoder_output']
|
||||
encoder_output_lengths = mel_input_lengths // encoder_downsampling_factor
|
||||
return encoder_output, encoder_output_lengths
|
||||
|
||||
|
||||
class WhisperDecoding:
|
||||
|
||||
def __init__(self, engine_dir, runtime_mapping, debug_mode=False):
|
||||
|
||||
self.decoder_config = read_config('decoder', engine_dir)
|
||||
self.decoder_generation_session = self.get_session(
|
||||
engine_dir, runtime_mapping, debug_mode)
|
||||
|
||||
def get_session(self, engine_dir, runtime_mapping, debug_mode=False):
|
||||
serialize_path = engine_dir / 'decoder' / 'rank0.engine'
|
||||
with open(serialize_path, "rb") as f:
|
||||
decoder_engine_buffer = f.read()
|
||||
|
||||
decoder_model_config = ModelConfig(
|
||||
max_batch_size=self.decoder_config['max_batch_size'],
|
||||
max_beam_width=self.decoder_config['max_beam_width'],
|
||||
num_heads=self.decoder_config['num_attention_heads'],
|
||||
num_kv_heads=self.decoder_config['num_attention_heads'],
|
||||
hidden_size=self.decoder_config['hidden_size'],
|
||||
vocab_size=self.decoder_config['vocab_size'],
|
||||
cross_attention=True,
|
||||
num_layers=self.decoder_config['num_hidden_layers'],
|
||||
gpt_attention_plugin=self.decoder_config['plugin_config']
|
||||
['gpt_attention_plugin'],
|
||||
remove_input_padding=self.decoder_config['plugin_config']
|
||||
['remove_input_padding'],
|
||||
kv_cache_type=KVCacheType.PAGED
|
||||
if self.decoder_config['plugin_config']['paged_kv_cache'] == True
|
||||
else KVCacheType.CONTINUOUS,
|
||||
has_position_embedding=self.
|
||||
decoder_config['has_position_embedding'],
|
||||
dtype=self.decoder_config['dtype'],
|
||||
has_token_type_embedding=False,
|
||||
)
|
||||
decoder_generation_session = tensorrt_llm.runtime.GenerationSession(
|
||||
decoder_model_config,
|
||||
decoder_engine_buffer,
|
||||
runtime_mapping,
|
||||
debug_mode=debug_mode)
|
||||
|
||||
return decoder_generation_session
|
||||
|
||||
def generate(self,
|
||||
decoder_input_ids,
|
||||
encoder_outputs,
|
||||
encoder_max_input_length,
|
||||
encoder_input_lengths,
|
||||
eot_id,
|
||||
max_new_tokens=40,
|
||||
num_beams=1):
|
||||
batch_size = decoder_input_ids.shape[0]
|
||||
decoder_input_lengths = torch.tensor([
|
||||
decoder_input_ids.shape[-1]
|
||||
for _ in range(decoder_input_ids.shape[0])
|
||||
],
|
||||
dtype=torch.int32,
|
||||
device='cuda')
|
||||
decoder_max_input_length = torch.max(decoder_input_lengths).item()
|
||||
|
||||
cross_attention_mask = torch.ones([
|
||||
batch_size, decoder_max_input_length + max_new_tokens,
|
||||
encoder_max_input_length
|
||||
]).int().cuda()
|
||||
# generation config
|
||||
sampling_config = SamplingConfig(end_id=eot_id,
|
||||
pad_id=eot_id,
|
||||
num_beams=num_beams)
|
||||
self.decoder_generation_session.setup(
|
||||
decoder_input_lengths.size(0),
|
||||
decoder_max_input_length,
|
||||
max_new_tokens,
|
||||
beam_width=num_beams,
|
||||
encoder_max_input_length=encoder_max_input_length)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
|
||||
decoder_input_ids = decoder_input_ids.type(torch.int32).cuda()
|
||||
if self.decoder_config['plugin_config']['remove_input_padding']:
|
||||
# 50256 is the index of <pad> for all whisper models' decoder
|
||||
WHISPER_PAD_TOKEN_ID = 50256
|
||||
decoder_input_ids = remove_tensor_padding(
|
||||
decoder_input_ids, pad_value=WHISPER_PAD_TOKEN_ID)
|
||||
if encoder_outputs.dim() == 3:
|
||||
encoder_output_lens = torch.full((encoder_outputs.shape[0], ),
|
||||
encoder_outputs.shape[1],
|
||||
dtype=torch.int32,
|
||||
device='cuda')
|
||||
|
||||
encoder_outputs = remove_tensor_padding(encoder_outputs,
|
||||
encoder_output_lens)
|
||||
output_ids = self.decoder_generation_session.decode(
|
||||
decoder_input_ids,
|
||||
decoder_input_lengths,
|
||||
sampling_config,
|
||||
encoder_output=encoder_outputs,
|
||||
encoder_input_lengths=encoder_input_lengths,
|
||||
cross_attention_mask=cross_attention_mask,
|
||||
)
|
||||
torch.cuda.synchronize()
|
||||
|
||||
# get the list of int from output_ids tensor
|
||||
output_ids = output_ids.cpu().numpy().tolist()
|
||||
return output_ids
|
||||
|
||||
|
||||
class WhisperTRTLLM(object):
|
||||
|
||||
def __init__(self, engine_dir, assets_dir=None, device=None, is_multilingual=False,
|
||||
language="en", task="transcribe"):
|
||||
world_size = 1
|
||||
runtime_rank = tensorrt_llm.mpi_rank()
|
||||
runtime_mapping = tensorrt_llm.Mapping(world_size, runtime_rank)
|
||||
torch.cuda.set_device(runtime_rank % runtime_mapping.gpus_per_node)
|
||||
engine_dir = Path(engine_dir)
|
||||
encoder_config = read_config('encoder', engine_dir)
|
||||
decoder_config = read_config('decoder', engine_dir)
|
||||
self.n_mels = encoder_config['n_mels']
|
||||
self.num_languages = encoder_config['num_languages']
|
||||
is_multilingual = (decoder_config['vocab_size'] >= 51865)
|
||||
|
||||
self.encoder = WhisperEncoding(engine_dir)
|
||||
self.decoder = WhisperDecoding(engine_dir,
|
||||
runtime_mapping,
|
||||
debug_mode=False)
|
||||
self.n_mels = self.encoder.n_mels
|
||||
# self.tokenizer = get_tokenizer(num_languages=self.encoder.num_languages,
|
||||
# tokenizer_dir=assets_dir)
|
||||
self.device = device
|
||||
self.tokenizer = get_tokenizer(
|
||||
is_multilingual,
|
||||
num_languages=self.num_languages,
|
||||
language=language,
|
||||
task=task,
|
||||
)
|
||||
self.filters = mel_filters(self.device, self.encoder.n_mels, assets_dir)
|
||||
|
||||
def log_mel_spectrogram(
|
||||
self,
|
||||
audio: Union[str, np.ndarray, torch.Tensor],
|
||||
padding: int = 0,
|
||||
return_duration=True
|
||||
):
|
||||
"""
|
||||
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 = (80 or 128, n_frames)
|
||||
A Tensor that contains the Mel spectrogram
|
||||
"""
|
||||
if not torch.is_tensor(audio):
|
||||
if isinstance(audio, str):
|
||||
if audio.endswith('.wav'):
|
||||
audio, _ = load_audio_wav_format(audio)
|
||||
else:
|
||||
audio = load_audio(audio)
|
||||
assert isinstance(audio, np.ndarray), f"Unsupported audio type: {type(audio)}"
|
||||
duration = audio.shape[-1] / SAMPLE_RATE
|
||||
audio = pad_or_trim(audio, N_SAMPLES)
|
||||
audio = audio.astype(np.float32)
|
||||
audio = torch.from_numpy(audio)
|
||||
|
||||
if self.device is not None:
|
||||
audio = audio.to(self.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
|
||||
|
||||
mel_spec = self.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
|
||||
if return_duration:
|
||||
return log_spec, duration
|
||||
else:
|
||||
return log_spec
|
||||
|
||||
def process_batch(
|
||||
self,
|
||||
mel,
|
||||
mel_input_lengths,
|
||||
text_prefix="<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
|
||||
num_beams=1,
|
||||
max_new_tokens=96):
|
||||
prompt_id = self.tokenizer.encode(
|
||||
text_prefix, allowed_special=set(self.tokenizer.special_tokens.keys()))
|
||||
|
||||
prompt_id = torch.tensor(prompt_id)
|
||||
batch_size = mel.shape[0]
|
||||
decoder_input_ids = prompt_id.repeat(batch_size, 1)
|
||||
|
||||
encoder_output, encoder_output_lengths = self.encoder.get_audio_features(mel, mel_input_lengths)
|
||||
encoder_max_input_length = torch.max(encoder_output_lengths).item()
|
||||
output_ids = self.decoder.generate(decoder_input_ids,
|
||||
encoder_output,
|
||||
encoder_max_input_length,
|
||||
encoder_output_lengths,
|
||||
self.tokenizer.eot,
|
||||
max_new_tokens=max_new_tokens,
|
||||
num_beams=num_beams)
|
||||
texts = []
|
||||
for i in range(len(output_ids)):
|
||||
text = self.tokenizer.decode(output_ids[i][0]).strip()
|
||||
texts.append(text)
|
||||
return texts
|
||||
|
||||
def transcribe(
|
||||
self,
|
||||
mel,
|
||||
text_prefix="<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
|
||||
dtype='float16',
|
||||
batch_size=1,
|
||||
num_beams=1,
|
||||
padding_strategy="max",
|
||||
):
|
||||
mel = mel.type(str_dtype_to_torch(dtype))
|
||||
mel = mel.unsqueeze(0)
|
||||
# repeat the mel spectrogram to match the batch size
|
||||
mel = mel.repeat(batch_size, 1, 1)
|
||||
if padding_strategy == "longest":
|
||||
pass
|
||||
else:
|
||||
mel = torch.nn.functional.pad(mel, (0, 3000 - mel.shape[2]))
|
||||
features_input_lengths = torch.full((mel.shape[0], ),
|
||||
mel.shape[2],
|
||||
dtype=torch.int32,
|
||||
device=mel.device)
|
||||
|
||||
predictions = self.process_batch(mel, features_input_lengths, text_prefix, num_beams)
|
||||
prediction = predictions[0]
|
||||
|
||||
# remove all special tokens in the prediction
|
||||
prediction = re.sub(r'<\|.*?\|>', '', prediction)
|
||||
return prediction.strip()
|
||||
|
||||
|
||||
def decode_wav_file(
|
||||
model,
|
||||
mel,
|
||||
text_prefix="<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
|
||||
dtype='float16',
|
||||
batch_size=1,
|
||||
num_beams=1,
|
||||
normalizer=None,
|
||||
mel_filters_dir=None):
|
||||
|
||||
mel = mel.type(str_dtype_to_torch(dtype))
|
||||
mel = mel.unsqueeze(0)
|
||||
# repeat the mel spectrogram to match the batch size
|
||||
mel = mel.repeat(batch_size, 1, 1)
|
||||
predictions = model.process_batch(mel, text_prefix, num_beams)
|
||||
prediction = predictions[0]
|
||||
|
||||
# remove all special tokens in the prediction
|
||||
prediction = re.sub(r'<\|.*?\|>', '', prediction)
|
||||
if normalizer:
|
||||
prediction = normalizer(prediction)
|
||||
|
||||
return prediction.strip()
|
||||
Reference in New Issue
Block a user