diff --git a/README.md b/README.md index 746986c..24060e7 100644 --- a/README.md +++ b/README.md @@ -44,6 +44,7 @@ Online Demo: # What's New 🔥 +- 2024/11: Add support for timestamp based on the CTC alignment. - 2024/7: Added Export Features for [ONNX](./demo_onnx.py) and [libtorch](./demo_libtorch.py), as well as Python Version Runtimes: [funasr-onnx-0.4.0](https://pypi.org/project/funasr-onnx/), [funasr-torch-0.1.1](https://pypi.org/project/funasr-torch/) - 2024/7: The [SenseVoice-Small](https://www.modelscope.cn/models/iic/SenseVoiceSmall) voice understanding model is open-sourced, which offers high-precision multilingual speech recognition, emotion recognition, and audio event detection capabilities for Mandarin, Cantonese, English, Japanese, and Korean and leads to exceptionally low inference latency. - 2024/7: The CosyVoice for natural speech generation with multi-language, timbre, and emotion control. CosyVoice excels in multi-lingual voice generation, zero-shot voice generation, cross-lingual voice cloning, and instruction-following capabilities. [CosyVoice repo](https://github.com/FunAudioLLM/CosyVoice) and [CosyVoice space](https://www.modelscope.cn/studios/iic/CosyVoice-300M). diff --git a/demo2.py b/demo2.py index 60f9dec..eee7ae3 100644 --- a/demo2.py +++ b/demo2.py @@ -21,3 +21,17 @@ res = m.inference( text = rich_transcription_postprocess(res[0][0]["text"]) print(text) + +res = m.inference( + data_in=f"{kwargs['model_path']}/example/en.mp3", + language="auto", # "zh", "en", "yue", "ja", "ko", "nospeech" + use_itn=False, + ban_emo_unk=False, + output_timestamp=True, + **kwargs, +) + +timestamp = res[0][0]["timestamp"] +text = rich_transcription_postprocess(res[0][0]["text"]) +print(text) +print(timestamp) diff --git a/model.py b/model.py index edacef0..a89defd 100644 --- a/model.py +++ b/model.py @@ -13,7 +13,7 @@ from funasr.train_utils.device_funcs import force_gatherable from funasr.losses.label_smoothing_loss import LabelSmoothingLoss from funasr.metrics.compute_acc import compute_accuracy, th_accuracy from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank - +from utils.ctc_alignment import ctc_forced_align class SinusoidalPositionEncoder(torch.nn.Module): """ """ @@ -830,6 +830,8 @@ class SenseVoiceSmall(nn.Module): ).repeat(speech.size(0), 1, 1) use_itn = kwargs.get("use_itn", False) + output_timestamp = kwargs.get("output_timestamp", False) + textnorm = kwargs.get("text_norm", None) if textnorm is None: textnorm = "withitn" if use_itn else "woitn" @@ -878,13 +880,45 @@ class SenseVoiceSmall(nn.Module): # Change integer-ids to tokens text = tokenizer.decode(token_int) - - result_i = {"key": key[i], "text": text} - results.append(result_i) - if ibest_writer is not None: ibest_writer["text"][key[i]] = text + if output_timestamp: + from itertools import groupby + timestamp = [] + tokens = tokenizer.text2tokens(text)[4:] + + logits_speech = self.ctc.softmax(encoder_out)[i, 4:encoder_out_lens[i].item(), :] + + pred = logits_speech.argmax(-1).cpu() + logits_speech[pred==self.blank_id, self.blank_id] = 0 + + align = ctc_forced_align( + logits_speech.unsqueeze(0).float(), + torch.Tensor(token_int[4:]).unsqueeze(0).long().to(logits_speech.device), + (encoder_out_lens-4).long(), + torch.tensor(len(token_int)-4).unsqueeze(0).long().to(logits_speech.device), + ignore_id=self.ignore_id, + ) + + pred = groupby(align[0, :encoder_out_lens[0]]) + _start = 0 + token_id = 0 + ts_max = encoder_out_lens[i] - 4 + for pred_token, pred_frame in pred: + _end = _start + len(list(pred_frame)) + if pred_token != 0: + ts_left = max((_start*60-30)/1000, 0) + ts_right = min((_end*60-30)/1000, (ts_max*60-30)/1000) + timestamp.append([tokens[token_id], ts_left, ts_right]) + token_id += 1 + _start = _end + + result_i = {"key": key[i], "text": text, "timestamp": timestamp} + results.append(result_i) + else: + result_i = {"key": key[i], "text": text} + results.append(result_i) return results, meta_data def export(self, **kwargs): diff --git a/utils/ctc_alignment.py b/utils/ctc_alignment.py new file mode 100644 index 0000000..f10fcf2 --- /dev/null +++ b/utils/ctc_alignment.py @@ -0,0 +1,76 @@ +import torch + +def ctc_forced_align( + log_probs: torch.Tensor, + targets: torch.Tensor, + input_lengths: torch.Tensor, + target_lengths: torch.Tensor, + blank: int = 0, + ignore_id: int = -1, +) -> torch.Tensor: + """Align a CTC label sequence to an emission. + + Args: + log_probs (Tensor): log probability of CTC emission output. + Tensor of shape `(B, T, C)`. where `B` is the batch size, `T` is the input length, + `C` is the number of characters in alphabet including blank. + targets (Tensor): Target sequence. Tensor of shape `(B, L)`, + where `L` is the target length. + input_lengths (Tensor): + Lengths of the inputs (max value must each be <= `T`). 1-D Tensor of shape `(B,)`. + target_lengths (Tensor): + Lengths of the targets. 1-D Tensor of shape `(B,)`. + blank_id (int, optional): The index of blank symbol in CTC emission. (Default: 0) + ignore_id (int, optional): The index of ignore symbol in CTC emission. (Default: -1) + """ + targets[targets == ignore_id] = blank + + batch_size, input_time_size, _ = log_probs.size() + bsz_indices = torch.arange(batch_size, device=input_lengths.device) + + _t_a_r_g_e_t_s_ = torch.cat( + ( + torch.stack((torch.full_like(targets, blank), targets), dim=-1).flatten(start_dim=1), + torch.full_like(targets[:, :1], blank), + ), + dim=-1, + ) + diff_labels = torch.cat( + ( + torch.as_tensor([[False, False]], device=targets.device).expand(batch_size, -1), + _t_a_r_g_e_t_s_[:, 2:] != _t_a_r_g_e_t_s_[:, :-2], + ), + dim=1, + ) + + neg_inf = torch.tensor(float("-inf"), device=log_probs.device, dtype=log_probs.dtype) + padding_num = 2 + padded_t = padding_num + _t_a_r_g_e_t_s_.size(-1) + best_score = torch.full((batch_size, padded_t), neg_inf, device=log_probs.device, dtype=log_probs.dtype) + best_score[:, padding_num + 0] = log_probs[:, 0, blank] + best_score[:, padding_num + 1] = log_probs[bsz_indices, 0, _t_a_r_g_e_t_s_[:, 1]] + + backpointers = torch.zeros((batch_size, input_time_size, padded_t), device=log_probs.device, dtype=targets.dtype) + + for t in range(1, input_time_size): + prev = torch.stack( + (best_score[:, 2:], best_score[:, 1:-1], torch.where(diff_labels, best_score[:, :-2], neg_inf)) + ) + prev_max_value, prev_max_idx = prev.max(dim=0) + best_score[:, padding_num:] = log_probs[:, t].gather(-1, _t_a_r_g_e_t_s_) + prev_max_value + backpointers[:, t, padding_num:] = prev_max_idx + + l1l2 = best_score.gather( + -1, torch.stack((padding_num + target_lengths * 2 - 1, padding_num + target_lengths * 2), dim=-1) + ) + + path = torch.zeros((batch_size, input_time_size), device=best_score.device, dtype=torch.long) + path[bsz_indices, input_lengths - 1] = padding_num + target_lengths * 2 - 1 + l1l2.argmax(dim=-1) + + for t in range(input_time_size - 1, 0, -1): + target_indices = path[:, t] + prev_max_idx = backpointers[bsz_indices, t, target_indices] + path[:, t - 1] += target_indices - prev_max_idx + + alignments = _t_a_r_g_e_t_s_.gather(dim=-1, index=(path - padding_num).clamp(min=0)) + return alignments