335 lines
10 KiB
Python
335 lines
10 KiB
Python
import itertools
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import subprocess
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import warnings
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, List
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import numba
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import numpy as np
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import torch
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import torch.nn.functional as F
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from .audio import HOP_LENGTH, SAMPLE_RATE, TOKENS_PER_SECOND
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from .tokenizer import Tokenizer
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if TYPE_CHECKING:
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from .model import Whisper
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def median_filter(x: torch.Tensor, filter_width: int):
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"""Apply a median filter of width `filter_width` along the last dimension of `x`"""
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pad_width = filter_width // 2
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if x.shape[-1] <= pad_width:
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# F.pad requires the padding width to be smaller than the input dimension
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return x
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if (ndim := x.ndim) <= 2:
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# `F.pad` does not support 1D or 2D inputs for reflect padding but supports 3D and 4D
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x = x[None, None, :]
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assert (
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filter_width > 0 and filter_width % 2 == 1
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), "`filter_width` should be an odd number"
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result = None
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x = F.pad(x, (filter_width // 2, filter_width // 2, 0, 0), mode="reflect")
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if x.is_cuda:
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try:
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from .triton_ops import median_filter_cuda
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result = median_filter_cuda(x, filter_width)
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except (RuntimeError, subprocess.CalledProcessError):
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warnings.warn(
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"Failed to launch Triton kernels, likely due to missing CUDA toolkit; "
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"falling back to a slower median kernel implementation..."
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)
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if result is None:
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# sort() is faster than torch.median (https://github.com/pytorch/pytorch/issues/51450)
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result = x.unfold(-1, filter_width, 1).sort()[0][..., filter_width // 2]
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if ndim <= 2:
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result = result[0, 0]
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return result
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@numba.jit
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def backtrace(trace: np.ndarray):
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i = trace.shape[0] - 1
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j = trace.shape[1] - 1
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trace[0, :] = 2
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trace[:, 0] = 1
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result = []
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while i > 0 or j > 0:
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result.append((i - 1, j - 1))
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if trace[i, j] == 0:
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i -= 1
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j -= 1
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elif trace[i, j] == 1:
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i -= 1
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elif trace[i, j] == 2:
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j -= 1
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else:
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raise ValueError("Unexpected trace[i, j]")
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result = np.array(result)
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return result[::-1, :].T
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@numba.jit(nopython=True, parallel=True)
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def dtw_cpu(x: np.ndarray):
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N, M = x.shape
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cost = np.ones((N + 1, M + 1), dtype=np.float32) * np.inf
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trace = -np.ones((N + 1, M + 1), dtype=np.float32)
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cost[0, 0] = 0
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for j in range(1, M + 1):
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for i in range(1, N + 1):
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c0 = cost[i - 1, j - 1]
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c1 = cost[i - 1, j]
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c2 = cost[i, j - 1]
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if c0 < c1 and c0 < c2:
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c, t = c0, 0
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elif c1 < c0 and c1 < c2:
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c, t = c1, 1
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else:
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c, t = c2, 2
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cost[i, j] = x[i - 1, j - 1] + c
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trace[i, j] = t
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return backtrace(trace)
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def dtw_cuda(x, BLOCK_SIZE=1024):
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from .triton_ops import dtw_kernel
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M, N = x.shape
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assert M < BLOCK_SIZE, f"M should be smaller than {BLOCK_SIZE=}"
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x_skew = (
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F.pad(x, (0, M + 1), value=np.inf).flatten()[: M * (N + M)].reshape(M, N + M)
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)
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x_skew = x_skew.T.contiguous()
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cost = torch.ones(N + M + 2, M + 2) * np.inf
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cost[0, 0] = 0
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cost = cost.cuda()
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trace = torch.zeros_like(cost, dtype=torch.int32)
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dtw_kernel[(1,)](
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cost,
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trace,
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x_skew,
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x_skew.stride(0),
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cost.stride(0),
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trace.stride(0),
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N,
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M,
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BLOCK_SIZE=BLOCK_SIZE,
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)
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trace = trace.T.flatten()[: (M + 1) * (M + N + 3)].reshape(M + 1, M + N + 3)[
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:, : N + 1
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]
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return backtrace(trace.cpu().numpy())
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def dtw(x: torch.Tensor) -> np.ndarray:
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if x.is_cuda:
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try:
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return dtw_cuda(x)
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except (RuntimeError, subprocess.CalledProcessError):
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warnings.warn(
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"Failed to launch Triton kernels, likely due to missing CUDA toolkit; "
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"falling back to a slower DTW implementation..."
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)
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return dtw_cpu(x.double().cpu().numpy())
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@dataclass
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class WordTiming:
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word: str
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tokens: List[int]
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start: float
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end: float
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probability: float
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def find_alignment(
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model: "Whisper",
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tokenizer: Tokenizer,
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text_tokens: List[int],
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mel: torch.Tensor,
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num_frames: int,
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*,
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medfilt_width: int = 7,
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qk_scale: float = 1.0,
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) -> List[WordTiming]:
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if len(text_tokens) == 0:
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return []
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tokens = torch.tensor(
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[
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*tokenizer.sot_sequence,
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tokenizer.no_timestamps,
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*text_tokens,
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tokenizer.eot,
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]
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).to(model.device)
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# install hooks on the cross attention layers to retrieve the attention weights
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QKs = [None] * model.dims.n_text_layer
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hooks = [
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block.cross_attn.register_forward_hook(
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lambda _, ins, outs, index=i: QKs.__setitem__(index, outs[-1][0])
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)
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for i, block in enumerate(model.decoder.blocks)
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]
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with torch.no_grad():
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logits = model(mel.unsqueeze(0), tokens.unsqueeze(0))[0]
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sampled_logits = logits[len(tokenizer.sot_sequence) :, : tokenizer.eot]
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token_probs = sampled_logits.softmax(dim=-1)
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text_token_probs = token_probs[np.arange(len(text_tokens)), text_tokens]
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text_token_probs = text_token_probs.tolist()
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for hook in hooks:
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hook.remove()
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# heads * tokens * frames
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weights = torch.stack([QKs[l][h] for l, h in model.alignment_heads.indices().T])
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weights = weights[:, :, : num_frames // 2]
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weights = (weights * qk_scale).softmax(dim=-1)
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std, mean = torch.std_mean(weights, dim=-2, keepdim=True, unbiased=False)
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weights = (weights - mean) / std
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weights = median_filter(weights, medfilt_width)
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matrix = weights.mean(axis=0)
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matrix = matrix[len(tokenizer.sot_sequence) : -1]
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text_indices, time_indices = dtw(-matrix)
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words, word_tokens = tokenizer.split_to_word_tokens(text_tokens + [tokenizer.eot])
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word_boundaries = np.pad(np.cumsum([len(t) for t in word_tokens[:-1]]), (1, 0))
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jumps = np.pad(np.diff(text_indices), (1, 0), constant_values=1).astype(bool)
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jump_times = time_indices[jumps] / TOKENS_PER_SECOND
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start_times = jump_times[word_boundaries[:-1]]
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end_times = jump_times[word_boundaries[1:]]
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word_probabilities = [
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np.mean(text_token_probs[i:j])
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for i, j in zip(word_boundaries[:-1], word_boundaries[1:])
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]
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# hack: ensure the first and second word is not longer than twice the median word duration.
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# a better segmentation algorithm based on VAD should be able to replace this.
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word_durations = end_times - start_times
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word_durations = word_durations[word_durations.nonzero()]
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if len(word_durations) > 0:
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median_duration = np.median(word_durations)
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max_duration = median_duration * 2
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if len(word_durations) >= 2 and word_durations[1] > max_duration:
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boundary = max(end_times[2] / 2, end_times[2] - max_duration)
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end_times[0] = start_times[1] = boundary
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if len(word_durations) >= 1 and end_times[0] - start_times[0] > max_duration:
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start_times[0] = max(0, end_times[0] - max_duration)
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return [
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WordTiming(word, tokens, start, end, probability)
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for word, tokens, start, end, probability in zip(
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words, word_tokens, start_times, end_times, word_probabilities
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)
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]
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def merge_punctuations(alignment: List[WordTiming], prepended: str, appended: str):
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# merge prepended punctuations
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i = len(alignment) - 2
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j = len(alignment) - 1
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while i >= 0:
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previous = alignment[i]
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following = alignment[j]
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if previous.word.startswith(" ") and previous.word.strip() in prepended:
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# prepend it to the following word
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following.word = previous.word + following.word
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following.tokens = previous.tokens + following.tokens
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previous.word = ""
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previous.tokens = []
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else:
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j = i
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i -= 1
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# merge appended punctuations
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i = 0
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j = 1
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while j < len(alignment):
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previous = alignment[i]
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following = alignment[j]
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if not previous.word.endswith(" ") and following.word in appended:
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# append it to the previous word
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previous.word = previous.word + following.word
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previous.tokens = previous.tokens + following.tokens
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following.word = ""
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following.tokens = []
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else:
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i = j
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j += 1
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def add_word_timestamps(
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*,
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segments: List[dict],
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model: "Whisper",
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tokenizer: Tokenizer,
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mel: torch.Tensor,
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num_frames: int,
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prepend_punctuations: str = "\"'“¿([{-",
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append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
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**kwargs,
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):
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if len(segments) == 0:
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return
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text_tokens_per_segment = [
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[token for token in segment["tokens"] if token < tokenizer.eot]
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for segment in segments
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]
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text_tokens = list(itertools.chain.from_iterable(text_tokens_per_segment))
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alignment = find_alignment(model, tokenizer, text_tokens, mel, num_frames, **kwargs)
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merge_punctuations(alignment, prepend_punctuations, append_punctuations)
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time_offset = segments[0]["seek"] * HOP_LENGTH / SAMPLE_RATE
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word_index = 0
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for segment, text_tokens in zip(segments, text_tokens_per_segment):
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saved_tokens = 0
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words = []
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while word_index < len(alignment) and saved_tokens < len(text_tokens):
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timing = alignment[word_index]
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if timing.word:
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words.append(
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dict(
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word=timing.word,
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start=round(time_offset + timing.start, 2),
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end=round(time_offset + timing.end, 2),
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probability=timing.probability,
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)
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)
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saved_tokens += len(timing.tokens)
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word_index += 1
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if len(words) > 0:
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# adjust the segment-level timestamps based on the word-level timestamps
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segment["start"] = words[0]["start"]
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segment["end"] = words[-1]["end"]
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segment["words"] = words
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