update with main (#1800)

zhifu gao

2024-06-11 20aa07268a7fafaaab7762b488615af32a0e82b4

update with main (#1800)

* add cmakelist

* add paraformer-torch

* add debug for funasr-onnx-offline

* fix redefinition of jieba StdExtension.hpp

* add loading torch models

* update funasr-onnx-offline

* add SwitchArg for wss-server

* add SwitchArg for funasr-onnx-offline

* update cmakelist

* update funasr-onnx-offline-rtf

* add define condition

* add gpu define for offlne-stream

* update com define

* update offline-stream

* update cmakelist

* update func CompileHotwordEmbedding

* add timestamp for paraformer-torch

* add C10_USE_GLOG for paraformer-torch

* update paraformer-torch

* fix func FunASRWfstDecoderInit

* update model.h

* fix func FunASRWfstDecoderInit

* fix tpass_stream

* update paraformer-torch

* add bladedisc for funasr-onnx-offline

* update comdefine

* update funasr-wss-server

* add log for torch

* fix GetValue BLADEDISC

* fix log

* update cmakelist

* update warmup to 10

* update funasrruntime

* add batch_size for wss-server

* add batch for bins

* add batch for offline-stream

* add batch for paraformer

* add batch for offline-stream

* fix func SetBatchSize

* add SetBatchSize for model

* add SetBatchSize for model

* fix func Forward

* fix padding

* update funasrruntime

* add dec reset for batch

* set batch default value

* add argv for CutSplit

* sort frame_queue

* sorted msgs

* fix FunOfflineInfer

* add dynamic batch for fetch

* fix FetchDynamic

* update run_server.sh

* update run_server.sh

* cpp http post server support (#1739)

* add cpp http server

* add some comment

* remove some comments

* del debug infos

* restore run_server.sh

* adapt to new model struct

* 修复了onnxruntime在macos下编译失败的错误 (#1748)

* Add files via upload

增加macos的编译支持

* Add files via upload

增加macos支持

* Add files via upload

target_link_directories(funasr PUBLIC ${ONNXRUNTIME_DIR}/lib)
target_link_directories(funasr PUBLIC ${FFMPEG_DIR}/lib)
添加 if(APPLE) 限制

---------

Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com>

* Delete docs/images/wechat.png

* Add files via upload

* fixed the issues about seaco-onnx timestamp

* fix bug (#1764)

当语音识别结果包含 `http` 时，标点符号预测会把它会被当成 url

* fix empty asr result (#1765)

解码结果为空的语音片段，text 用空字符串

* docs

* docs

* docs

* docs

* docs

* keep empty speech result (#1772)

* docs

* docs

* update wechat QRcode

* Add python funasr api support for websocket srv (#1777)

* add python funasr_api supoort

* change little to README.md

* add core tools stream

* modified a little

* fix bug for timeout

* support for buffer decode

* add ffmpeg decode for buffer

* auto frontend

* auto frontend

* auto frontend

* auto frontend

* auto frontend

* auto frontend

* auto frontend

* auto frontend

* Dev gzf exp (#1785)

* resume from step

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* train_loss_avg train_acc_avg

* train_loss_avg train_acc_avg

* train_loss_avg train_acc_avg

* log step

* wav is not exist

* wav is not exist

* decoding

* decoding

* decoding

* wechat

* decoding key

* decoding key

* decoding key

* decoding key

* decoding key

* decoding key

* dynamic batch

* start_data_split_i=0

* total_time/accum_grad

* total_time/accum_grad

* total_time/accum_grad

* update avg slice

* update avg slice

* sensevoice sanm

* sensevoice sanm

* sensevoice sanm

---------

Co-authored-by: 北念 <lzr265946@alibaba-inc.com>

* auto frontend

* update paraformer timestamp

* add cif_v1 and cif_export

* Update SDK_advanced_guide_offline_zh.md

* add cif_wo_hidden_v1

* [fix] fix empty asr result (#1794)

* wechat

* [fix] better solution for handling empty result (#1796)

---------

Co-authored-by: 雾聪 <wucong.lyb@alibaba-inc.com>
Co-authored-by: zhaomingwork <61895407+zhaomingwork@users.noreply.github.com>
Co-authored-by: szsteven008 <97944818+szsteven008@users.noreply.github.com>
Co-authored-by: Ephemeroptera <605686962@qq.com>
Co-authored-by: 彭震东 <zhendong.peng@qq.com>
Co-authored-by: Shi Xian <40013335+R1ckShi@users.noreply.github.com>
Co-authored-by: 维石 <shixian.shi@alibaba-inc.com>
Co-authored-by: 北念 <lzr265946@alibaba-inc.com>
Co-authored-by: zhuangzhong <zhuangzhong@corp.netease.com>
Co-authored-by: Xingchen Song(宋星辰) <xingchensong1996@163.com>

 docs/images/wechat.png

 funasr/auto/auto_model.py

@@ -429,6 +429,10 @@
            #                      f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
            #                      f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")

            if len(results_sorted) != n:
                results_ret_list.append({"key": key, "text": "", "timestamp": []})
                logging.info("decoding, utt: {}, empty result".format(key))
                continue
            restored_data = [0] * n
            for j in range(n):
                index = sorted_data[j][1]

 funasr/models/llm_asr/adaptor.py

@@ -125,6 +125,7 @@
        olens = None
        olens = (ilens - 1) // self.k + 1
        masks = (~make_pad_mask(olens)[:, None, :]).to(x.device)

        if self.blocks is not None:
            for layer, block in enumerate(self.blocks):
                x, masks = block(x, masks)

 funasr/models/paraformer/cif_predictor.py

@@ -80,7 +80,7 @@
                    hidden, alphas, token_num, mask=mask

                )



            acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)

            acoustic_embeds, cif_peak = cif_v1(hidden, alphas, self.threshold)



            if target_length is None and self.tail_threshold > 0.0:

                token_num_int = torch.max(token_num).type(torch.int32).item()

@@ -245,7 +245,7 @@
                        hidden, alphas, token_num, mask=None

                    )



            acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)

            acoustic_embeds, cif_peak = cif_v1(hidden, alphas, self.threshold)

            if target_length is None and self.tail_threshold > 0.0:

                token_num_int = torch.max(token_num).type(torch.int32).item()

                acoustic_embeds = acoustic_embeds[:, :token_num_int, :]

@@ -449,7 +449,7 @@
        mask = mask.transpose(-1, -2).float()

        mask = mask.squeeze(-1)

        hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, mask=mask)

        acoustic_embeds, cif_peak = cif_export(hidden, alphas, self.threshold)

        acoustic_embeds, cif_peak = cif_v1_export(hidden, alphas, self.threshold)



        return acoustic_embeds, token_num, alphas, cif_peak



@@ -494,7 +494,60 @@
        token_num_floor = torch.floor(token_num)



        return hidden, alphas, token_num_floor

@torch.jit.script

def cif_v1_export(hidden, alphas, threshold: float):

    device = hidden.device

    dtype = hidden.dtype

    batch_size, len_time, hidden_size = hidden.size()

    threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)



    frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)

    fires = torch.zeros(batch_size, len_time, dtype=dtype, device=device)



    prefix_sum = torch.cumsum(alphas, dim=1)

    prefix_sum_floor = torch.floor(prefix_sum)

    dislocation_prefix_sum = torch.roll(prefix_sum, 1, dims=1)

    dislocation_prefix_sum_floor = torch.floor(dislocation_prefix_sum)



    dislocation_prefix_sum_floor[:, 0] = 0

    dislocation_diff = prefix_sum_floor - dislocation_prefix_sum_floor



    fire_idxs = dislocation_diff > 0

    fires[fire_idxs] = 1

    fires = fires + prefix_sum - prefix_sum_floor



    prefix_sum_hidden = torch.cumsum(

        alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1

    )



    frames = prefix_sum_hidden[fire_idxs]

    shift_frames = torch.roll(frames, 1, dims=0)



    batch_len = fire_idxs.sum(1)

    batch_idxs = torch.cumsum(batch_len, dim=0)

    shift_batch_idxs = torch.roll(batch_idxs, 1, dims=0)

    shift_batch_idxs[0] = 0

    shift_frames[shift_batch_idxs] = 0



    remains = fires - torch.floor(fires)

    remain_frames = (

        remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]

    )



    shift_remain_frames = torch.roll(remain_frames, 1, dims=0)

    shift_remain_frames[shift_batch_idxs] = 0



    frames = frames - shift_frames + shift_remain_frames - remain_frames



    max_label_len = batch_len.max()



    frame_fires = torch.zeros(

        batch_size, max_label_len, hidden_size, dtype=dtype, device=device

    )

    indices = torch.arange(max_label_len, device=device).expand(batch_size, -1)

    frame_fires_idxs = indices < batch_len.unsqueeze(1)

    frame_fires[frame_fires_idxs] = frames

    return frame_fires, fires



@torch.jit.script

def cif_export(hidden, alphas, threshold: float):

@@ -608,6 +661,74 @@
    return torch.stack(list_ls, 0), fires





def cif_wo_hidden_v1(alphas, threshold, return_fire_idxs=False):

    batch_size, len_time = alphas.size()

    device = alphas.device

    dtype = alphas.dtype



    threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)



    fires = torch.zeros(batch_size, len_time, dtype=dtype, device=device)



    prefix_sum = torch.cumsum(alphas, dim=1)

    prefix_sum_floor = torch.floor(prefix_sum)

    dislocation_prefix_sum = torch.roll(prefix_sum, 1, dims=1)

    dislocation_prefix_sum_floor = torch.floor(dislocation_prefix_sum)



    dislocation_prefix_sum_floor[:, 0] = 0

    dislocation_diff = prefix_sum_floor - dislocation_prefix_sum_floor



    fire_idxs = dislocation_diff > 0

    fires[fire_idxs] = 1

    fires = fires + prefix_sum - prefix_sum_floor

    if return_fire_idxs:

        return fires, fire_idxs

    return fires





def cif_v1(hidden, alphas, threshold):

    fires, fire_idxs = cif_wo_hidden_v1(alphas, threshold, return_fire_idxs=True)



    device = hidden.device

    dtype = hidden.dtype

    batch_size, len_time, hidden_size = hidden.size()

    frames = torch.zeros(batch_size, len_time, hidden_size,

                         dtype=dtype, device=device)

    prefix_sum_hidden = torch.cumsum(

        alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1

    )



    frames = prefix_sum_hidden[fire_idxs]

    shift_frames = torch.roll(frames, 1, dims=0)



    batch_len = fire_idxs.sum(1)

    batch_idxs = torch.cumsum(batch_len, dim=0)

    shift_batch_idxs = torch.roll(batch_idxs, 1, dims=0)

    shift_batch_idxs[0] = 0

    shift_frames[shift_batch_idxs] = 0



    remains = fires - torch.floor(fires)

    remain_frames = (

        remains[fire_idxs].unsqueeze(-1).tile((1,

                                               hidden_size)) * hidden[fire_idxs]

    )



    shift_remain_frames = torch.roll(remain_frames, 1, dims=0)

    shift_remain_frames[shift_batch_idxs] = 0



    frames = frames - shift_frames + shift_remain_frames - remain_frames



    max_label_len = batch_len.max()



    frame_fires = torch.zeros(

        batch_size, max_label_len, hidden_size, dtype=dtype, device=device

    )

    indices = torch.arange(max_label_len, device=device).expand(batch_size, -1)

    frame_fires_idxs = indices < batch_len.unsqueeze(1)

    frame_fires[frame_fires_idxs] = frames

    return frame_fires, fires





def cif_wo_hidden(alphas, threshold):

    batch_size, len_time = alphas.size()




 funasr/models/paraformer/model.py

@@ -4,6 +4,7 @@
#  MIT License  (https://opensource.org/licenses/MIT)

import time
import copy
import torch
import logging
from torch.cuda.amp import autocast
@@ -21,6 +22,7 @@
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
from funasr.models.transformer.utils.nets_utils import make_pad_mask
from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank


@@ -452,6 +454,7 @@
        is_use_lm = (
            kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
        )
        pred_timestamp = kwargs.get("pred_timestamp", False)
        if self.beam_search is None and (is_use_lm or is_use_ctc):
            logging.info("enable beam_search")
            self.init_beam_search(**kwargs)
@@ -506,6 +509,7 @@
            predictor_outs[2],
            predictor_outs[3],
        )
        
        pre_token_length = pre_token_length.round().long()
        if torch.max(pre_token_length) < 1:
            return []
@@ -564,9 +568,21 @@
                    # Change integer-ids to tokens
                    token = tokenizer.ids2tokens(token_int)
                    text_postprocessed = tokenizer.tokens2text(token)
                    
                    if pred_timestamp:
                        timestamp_str, timestamp = ts_prediction_lfr6_standard(
                            pre_peak_index[i],
                            alphas[i],
                            copy.copy(token),
                            vad_offset=kwargs.get("begin_time", 0),
                            upsample_rate=1,
                        )
                        if not hasattr(tokenizer, "bpemodel"):
                            text_postprocessed, time_stamp_postprocessed, _ = postprocess_utils.sentence_postprocess(token, timestamp)
                        result_i = {"key": key[i], "text": text_postprocessed, "timestamp": time_stamp_postprocessed,}
                    else:
                    if not hasattr(tokenizer, "bpemodel"):
                        text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)

                    result_i = {"key": key[i], "text": text_postprocessed}

                    if ibest_writer is not None:

 funasr/utils/timestamp_tools.py

@@ -29,13 +29,13 @@


def ts_prediction_lfr6_standard(
    us_alphas, us_peaks, char_list, vad_offset=0.0, force_time_shift=-1.5, sil_in_str=True
    us_alphas, us_peaks, char_list, vad_offset=0.0, force_time_shift=-1.5, sil_in_str=True, upsample_rate=3,
):
    if not len(char_list):
        return "", []
    START_END_THRESHOLD = 5
    MAX_TOKEN_DURATION = 12
    TIME_RATE = 10.0 * 6 / 1000 / 3  #  3 times upsampled
    MAX_TOKEN_DURATION = 12  #  3 times upsampled
    TIME_RATE=10.0 * 6 / 1000 / upsample_rate
    if len(us_alphas.shape) == 2:
        alphas, peaks = us_alphas[0], us_peaks[0]  # support inference batch_size=1 only
    else:

 runtime/docs/SDK_advanced_guide_offline_zh.md

@@ -149,6 +149,7 @@
--port 10095 部署端口号
--wav-path 需要进行转写的音频文件，支持文件路径
--hotword 热词文件，每行一个热词，格式(热词 权重)：阿里巴巴 20
--thread-num 设置客户端线程数
--use-itn 设置是否使用itn，默认1开启，设置为0关闭
```