Merge 66341adf5583a5a762e2c45fefc054617d8b5ea9 into 35e755427da174037da246642cab6987876c74fa

argparse_tools.py

@@ -0,0 +1,83 @@
+import argparse
+from pathlib import Path
+
+import yaml
+import sys
+
+
+class ArgumentParser(argparse.ArgumentParser):
+    """Simple implementation of ArgumentParser supporting config file
+
+    This class is originated from https://github.com/bw2/ConfigArgParse,
+    but this class is lack of some features that it has.
+
+    - Not supporting multiple config files
+    - Automatically adding "--config" as an option.
+    - Not supporting any formats other than yaml
+    - Not checking argument type
+
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.add_argument("--config", help="Give config file in yaml format")
+
+    def parse_known_args(self, args=None, namespace=None):
+        # Once parsing for setting from "--config"
+        _args, _ = super().parse_known_args(args, namespace)
+        if _args.config is not None:
+            if not Path(_args.config).exists():
+                self.error(f"No such file: {_args.config}")
+
+            with open(_args.config, "r", encoding="utf-8") as f:
+                d = yaml.safe_load(f)
+            if not isinstance(d, dict):
+                self.error("Config file has non dict value: {_args.config}")
+
+            for key in d:
+                for action in self._actions:
+                    if key == action.dest:
+                        break
+                else:
+                    self.error(f"unrecognized arguments: {key} (from {_args.config})")
+
+            # NOTE(kamo): Ignore "--config" from a config file
+            # NOTE(kamo): Unlike "configargparse", this module doesn't check type.
+            #   i.e. We can set any type value regardless of argument type.
+            self.set_defaults(**d)
+        return super().parse_known_args(args, namespace)
+
+
+def get_commandline_args():
+    extra_chars = [
+        " ",
+        ";",
+        "&",
+        "(",
+        ")",
+        "|",
+        "^",
+        "<",
+        ">",
+        "?",
+        "*",
+        "[",
+        "]",
+        "$",
+        "`",
+        '"',
+        "\\",
+        "!",
+        "{",
+        "}",
+    ]
+
+    # Escape the extra characters for shell
+    argv = [
+        arg.replace("'", "'\\''")
+        if all(char not in arg for char in extra_chars)
+        else "'" + arg.replace("'", "'\\''") + "'"
+        for arg in sys.argv
+    ]
+
+    return sys.executable + " " + " ".join(argv)

subtitle_utils.py

@@ -0,0 +1,130 @@
+def time_convert(ms):
+    ms = int(ms)
+    tail = ms % 1000
+    s = ms // 1000
+    mi = s // 60
+    s = s % 60
+    h = mi // 60
+    mi = mi % 60
+    h = "00" if h == 0 else str(h)
+    mi = "00" if mi == 0 else str(mi)
+    s = "00" if s == 0 else str(s)
+    tail = str(tail)
+    if len(h) == 1: h = '0' + h
+    if len(mi) == 1: mi = '0' + mi
+    if len(s) == 1: s = '0' + s
+    return "{}:{}:{},{}".format(h, mi, s, tail)
+
+
+class Text2SRT():
+    def __init__(self, text_seg, ts_list, offset=0):
+        self.token_list = [i for i in text_seg.split() if len(i)]
+        self.ts_list = ts_list
+        start, end = ts_list[0][0] - offset, ts_list[-1][1] - offset
+        self.start_sec, self.end_sec = start, end
+        self.start_time = time_convert(start)
+        self.end_time = time_convert(end)
+    def text(self):
+        res = ""
+        for word in self.token_list:
+            if '\u4e00' <= word <= '\u9fff':
+                res += word
+            else:
+                res += " " + word
+        return res
+    def len(self):
+        return len(self.token_list)
+    def srt(self, acc_ost=0.0):
+        return "{} --> {}\n{}\n".format(
+            time_convert(self.start_sec+acc_ost*1000),
+            time_convert(self.end_sec+acc_ost*1000), 
+            self.text())
+    def time(self, acc_ost=0.0):
+        return (self.start_sec/1000+acc_ost, self.end_sec/1000+acc_ost)
+
+def distribute_spk(sentence_list, sd_time_list):
+    sd_sentence_list = []
+    for d in sentence_list:
+        sentence_start = d['ts_list'][0][0]
+        sentence_end = d['ts_list'][-1][1]
+        sentence_spk = 0
+        max_overlap = 0
+        for sd_time in sd_time_list:
+            spk_st, spk_ed, spk = sd_time
+            spk_st = spk_st*1000
+            spk_ed = spk_ed*1000
+            overlap = max(
+                min(sentence_end, spk_ed) - max(sentence_start, spk_st), 0)
+            if overlap > max_overlap:
+                max_overlap = overlap
+                sentence_spk = spk
+        d['spk'] = sentence_spk
+        sd_sentence_list.append(d)
+    return sd_sentence_list
+
+def generate_srt(sentence_list):
+    srt_total = ''
+    for i, d in enumerate(sentence_list):
+        t2s = Text2SRT(d['text_seg'], d['ts_list'])
+        if 'spk' in d:
+            srt_total += "{}  spk{}\n{}".format(i, d['spk'], t2s.srt())
+        else:
+            srt_total += "{}\n{}".format(i, t2s.srt())
+    return srt_total
+
+def generate_srt_clip(sentence_list, start, end, begin_index=0, time_acc_ost=0.0):
+    start, end = int(start * 1000), int(end * 1000)
+    srt_total = ''
+    cc = 1 + begin_index
+    subs = []
+    for i, d in enumerate(sentence_list):
+        if d['ts_list'][-1][1] <= start:
+            continue
+        if d['ts_list'][0][0] >= end:
+            break
+        # parts in between
+        if (d['ts_list'][-1][1] <= end and d['ts_list'][0][0] > start) or (d['ts_list'][-1][1] == end and d['ts_list'][0][0] == start):
+            t2s = Text2SRT(d['text_seg'], d['ts_list'], offset=start)
+            srt_total += "{}\n{}".format(cc, t2s.srt(time_acc_ost))
+            subs.append((t2s.time(time_acc_ost), t2s.text()))
+            cc += 1
+            continue
+        if d['ts_list'][0][0] <= start:
+            if not d['ts_list'][-1][1] > end:
+                for j, ts in enumerate(d['ts_list']):
+                    if ts[1] > start:
+                        break
+                _text = " ".join(d['text_seg'].split()[j:])
+                _ts = d['ts_list'][j:]
+            else:
+                for j, ts in enumerate(d['ts_list']):
+                    if ts[1] > start:
+                        _start = j
+                        break
+                for j, ts in enumerate(d['ts_list']):
+                    if ts[1] > end:
+                        _end = j
+                        break
+                _text = " ".join(d['text_seg'].split()[_start:_end])
+                _ts = d['ts_list'][_start:_end]
+            if len(ts):
+                t2s = Text2SRT(_text, _ts, offset=start)
+                srt_total += "{}\n{}".format(cc, t2s.srt(time_acc_ost))
+                subs.append((t2s.time(time_acc_ost), t2s.text()))
+                cc += 1
+            continue
+        if d['ts_list'][-1][1] > end:
+            for j, ts in enumerate(d['ts_list']):
+                if ts[1] > end:
+                    break
+            _text = " ".join(d['text_seg'].split()[:j])
+            _ts = d['ts_list'][:j]
+            if len(_ts):
+                t2s = Text2SRT(_text, _ts, offset=start)
+                srt_total += "{}\n{}".format(cc, t2s.srt(time_acc_ost))
+                subs.append(
+                    (t2s.time(time_acc_ost), t2s.text())
+                    )
+                cc += 1
+            continue
+    return srt_total, subs, cc

trans_utils.py

@@ -0,0 +1,82 @@
+PUNC_LIST = ['，', '。', '！', '？', '、']
+
+
+def pre_proc(text):
+    res = ''
+    for i in range(len(text)):
+        if text[i] in PUNC_LIST:
+            continue
+        if '\u4e00' <= text[i] <= '\u9fff':
+            if len(res) and res[-1] != " ":
+                res += ' ' + text[i]+' '
+            else:
+                res += text[i]+' '
+        else:
+            res += text[i]
+    if res[-1] == ' ':
+        res = res[:-1]
+    return res
+
+def proc(raw_text, timestamp, dest_text):
+    # simple matching
+    ld = len(dest_text.split())
+    mi, ts = [], []
+    offset = 0
+    while True:
+        fi = raw_text.find(dest_text, offset, len(raw_text))
+        # import pdb; pdb.set_trace()
+        ti = raw_text[:fi].count(' ')
+        if fi == -1:
+            break
+        offset = fi + ld
+        mi.append(fi)
+        ts.append([timestamp[ti][0]*16, timestamp[ti+ld-1][1]*16])
+        # import pdb; pdb.set_trace()
+    return ts
+
+def proc_spk(dest_spk, sd_sentences):
+    ts = []
+    for d in sd_sentences:
+        d_start = d['ts_list'][0][0]
+        d_end = d['ts_list'][-1][1]
+        spkid=dest_spk[3:]
+        if str(d['spk']) == spkid and d_end-d_start>999:
+            ts.append([d['start']*16, d['end']*16])
+    return ts
+
+def generate_vad_data(data, sd_sentences, sr=16000):
+    assert len(data.shape) == 1
+    vad_data = []
+    for d in sd_sentences:
+        d_start = round(d['ts_list'][0][0]/1000, 2)
+        d_end = round(d['ts_list'][-1][1]/1000, 2)
+        vad_data.append([d_start, d_end, data[int(d_start * sr):int(d_end * sr)]])
+    return vad_data
+
+def write_state(output_dir, state):
+    for key in ['/recog_res_raw', '/timestamp', '/sentences', '/sd_sentences']:
+        with open(output_dir+key, 'w') as fout:
+            fout.write(str(state[key[1:]]))
+    if 'sd_sentences' in state:
+        with open(output_dir+'/sd_sentences', 'w') as fout:
+            fout.write(str(state['sd_sentences']))
+
+import os
+def load_state(output_dir):
+    state = {}
+    with open(output_dir+'/recog_res_raw') as fin:
+        line = fin.read()
+        state['recog_res_raw'] = line
+    with open(output_dir+'/timestamp') as fin:
+        line = fin.read()
+        state['timestamp'] = eval(line)
+    with open(output_dir+'/sentences') as fin:
+        line = fin.read()
+        state['sentences'] = eval(line)
+    if os.path.exists(output_dir+'/sd_sentences'):
+        with open(output_dir+'/sd_sentences') as fin:
+            line = fin.read()
+            state['sd_sentences'] = eval(line)
+    return state
+        
+    

videoclipper.py

@@ -0,0 +1,329 @@
+import sys
+import copy
+import librosa
+import logging
+import argparse
+import numpy as np
+import soundfile as sf
+import moviepy.editor as mpy
+# from modelscope.pipelines import pipeline
+# from modelscope.utils.constant import Tasks
+from subtitle_utils import generate_srt, generate_srt_clip, distribute_spk
+from trans_utils import pre_proc, proc, write_state, load_state, proc_spk, generate_vad_data
+from argparse_tools import ArgumentParser, get_commandline_args
+
+from moviepy.editor import *
+from moviepy.video.tools.subtitles import SubtitlesClip
+
+
+class VideoClipper():
+    def __init__(self, asr_pipeline, sd_pipeline=None):
+        logging.warning("Initializing VideoClipper.")
+        self.asr_pipeline = asr_pipeline
+        self.sd_pipeline = sd_pipeline
+
+    def recog(self, audio_input, sd_switch='no', state=None):
+        if state is None:
+            state = {}
+        sr, data = audio_input
+        assert sr == 16000, "16kHz sample rate required, {} given.".format(sr)
+        if len(data.shape) == 2:  # multi-channel wav input
+            logging.warning("Input wav shape: {}, only first channel reserved.").format(data.shape)
+            data = data[:,0]
+        state['audio_input'] = (sr, data)
+        data = data.astype(np.float64)
+        rec_result = self.asr_pipeline(audio_in=data)
+        if sd_switch == 'yes':
+            vad_data = generate_vad_data(data.astype(np.float32), rec_result['sentences'], sr)
+            sd_result = self.sd_pipeline(audio=vad_data, batch_size=1)
+            rec_result['sd_sentences'] = distribute_spk(rec_result['sentences'], sd_result['text'])
+            res_srt = generate_srt(rec_result['sd_sentences'])
+            state['sd_sentences'] = rec_result['sd_sentences']
+        else:
+            res_srt = generate_srt(rec_result['sentences'])
+        state['recog_res_raw'] = rec_result['text_postprocessed']
+        state['timestamp'] = rec_result['time_stamp']
+        state['sentences'] = rec_result['sentences']
+        res_text = rec_result['text']
+        return res_text, res_srt, state
+
+    def clip(self, dest_text, start_ost, end_ost, state, dest_spk=None):
+        # get from state
+        audio_input = state['audio_input']
+        recog_res_raw = state['recog_res_raw']
+        timestamp = state['timestamp']
+        sentences = state['sentences']
+        sr, data = audio_input
+        data = data.astype(np.float64)
+
+        all_ts = []
+        if dest_spk is None or dest_spk == '' or 'sd_sentences' not in state:
+            for _dest_text in dest_text.split('#'):
+                _dest_text = pre_proc(_dest_text)
+                ts = proc(recog_res_raw, timestamp, _dest_text)
+                for _ts in ts: all_ts.append(_ts)
+        else:
+            for _dest_spk in dest_spk.split('#'):
+                ts = proc_spk(_dest_spk, state['sd_sentences'])
+                for _ts in ts: all_ts.append(_ts)
+        ts = all_ts
+        # ts.sort()
+        srt_index = 0
+        clip_srt = ""
+        if len(ts):
+            start, end = ts[0]
+            start = min(max(0, start+start_ost*16), len(data))
+            end = min(max(0, end+end_ost*16), len(data))
+            res_audio = data[start:end]
+            start_end_info = "from {} to {}".format(start/16000, end/16000)
+            srt_clip, _, srt_index = generate_srt_clip(sentences, start/16000.0, end/16000.0, begin_index=srt_index)
+            clip_srt += srt_clip
+            for _ts in ts[1:]:  # multiple sentence input or multiple output matched
+                start, end = _ts
+                start = min(max(0, start+start_ost*16), len(data))
+                end = min(max(0, end+end_ost*16), len(data))
+                start_end_info += ", from {} to {}".format(start, end)
+                res_audio = np.concatenate([res_audio, data[start+start_ost*16:end+end_ost*16]], -1)
+                srt_clip, _, srt_index = generate_srt_clip(sentences, start/16000.0, end/16000.0, begin_index=srt_index-1)
+                clip_srt += srt_clip
+        if len(ts):
+            message = "{} periods found in the speech: ".format(len(ts)) + start_end_info
+        else:
+            message = "No period found in the speech, return raw speech. You may check the recognition result and try other destination text."
+            res_audio = data
+        return (sr, res_audio), message, clip_srt
+
+    def video_recog(self, vedio_filename, sd_switch='no'):
+        vedio_filename = vedio_filename
+        clip_video_file = vedio_filename[:-4] + '_clip.mp4'
+        video = mpy.VideoFileClip(vedio_filename)
+        audio_file = vedio_filename[:-3] + 'wav'
+        video.audio.write_audiofile(audio_file)
+        wav = librosa.load(audio_file, sr=16000)[0]
+        state = {
+            'vedio_filename': vedio_filename,
+            'clip_video_file': clip_video_file,
+            'video': video,
+        }
+        # res_text, res_srt = self.recog((16000, wav), state)
+        return self.recog((16000, wav), sd_switch, state)
+
+    def video_clip(self, dest_text, start_ost, end_ost, state, font_size=32, font_color='white', add_sub=False, dest_spk=None):
+        # get from state
+        recog_res_raw = state['recog_res_raw']
+        timestamp = state['timestamp']
+        sentences = state['sentences']
+        video = state['video']
+        clip_video_file = state['clip_video_file']
+        vedio_filename = state['vedio_filename']
+        
+        all_ts = []
+        srt_index = 0
+        if dest_spk is None or dest_spk == '' or 'sd_sentences' not in state:
+            for _dest_text in dest_text.split('#'):
+                _dest_text = pre_proc(_dest_text)
+                ts = proc(recog_res_raw, timestamp, _dest_text)
+                for _ts in ts: all_ts.append(_ts)
+        else:
+            for _dest_spk in dest_spk.split('#'):
+                ts = proc_spk(_dest_spk, state['sd_sentences'])
+                for _ts in ts: all_ts.append(_ts)
+        time_acc_ost = 0.0
+        ts = all_ts
+        # ts.sort()
+        clip_srt = ""
+        if len(ts):
+            start, end = ts[0][0] / 16000, ts[0][1] / 16000
+            srt_clip, subs, srt_index = generate_srt_clip(sentences, start, end, begin_index=srt_index, time_acc_ost=time_acc_ost)
+            start, end = start+start_ost/1000.0, end+end_ost/1000.0
+            video_clip = video.subclip(start, end)
+            start_end_info = "from {} to {}".format(start, end)
+            clip_srt += srt_clip
+            if add_sub:
+                generator = lambda txt: TextClip(txt, font='./font/STHeitiMedium.ttc', fontsize=font_size, color=font_color)
+                subtitles = SubtitlesClip(subs, generator)
+                video_clip = CompositeVideoClip([video_clip, subtitles.set_pos(('center','bottom'))])
+            concate_clip = [video_clip]
+            time_acc_ost += end+end_ost/1000.0 - (start+start_ost/1000.0)
+            for _ts in ts[1:]:
+                start, end = _ts[0] / 16000, _ts[1] / 16000
+                srt_clip, subs, srt_index = generate_srt_clip(sentences, start, end, begin_index=srt_index-1, time_acc_ost=time_acc_ost)
+                chi_subs = []
+                sub_starts = subs[0][0][0]
+                for sub in subs:
+                    chi_subs.append(((sub[0][0]-sub_starts, sub[0][1]-sub_starts), sub[1]))
+                start, end = start+start_ost/1000.0, end+end_ost/1000.0
+                _video_clip = video.subclip(start, end)
+                start_end_info += ", from {} to {}".format(start, end)
+                clip_srt += srt_clip
+                if add_sub:
+                    generator = lambda txt: TextClip(txt, font='./font/STHeitiMedium.ttc', fontsize=font_size, color=font_color)
+                    subtitles = SubtitlesClip(chi_subs, generator)
+                    _video_clip = CompositeVideoClip([_video_clip, subtitles.set_pos(('center','bottom'))])
+                    # _video_clip.write_videofile("debug.mp4", audio_codec="aac")
+                concate_clip.append(copy.copy(_video_clip))
+                time_acc_ost += end+end_ost/1000.0 - (start+start_ost/1000.0)
+            message = "{} periods found in the audio: ".format(len(ts)) + start_end_info
+            logging.warning("Concating...")
+            if len(concate_clip) > 1:
+                video_clip = concatenate_videoclips(concate_clip)
+            video_clip.write_videofile(clip_video_file, audio_codec="aac")
+        else:
+            clip_video_file = vedio_filename
+            message = "No period found in the audio, return raw speech. You may check the recognition result and try other destination text."
+            srt_clip = ''
+        return clip_video_file, message, clip_srt
+
+
+def get_parser():
+    parser = ArgumentParser(
+        description="ClipVideo Argument",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "--stage",
+        type=int,
+        choices=(1, 2),
+        help="Stage, 0 for recognizing and 1 for clipping",
+        required=True
+    )
+    parser.add_argument(
+        "--file",
+        type=str,
+        default=None,
+        help="Input file path",
+        required=True
+    )
+    parser.add_argument(
+        "--sd_switch",
+        type=str,
+        choices=("no", "yes"),
+        default="no",
+        help="Trun on the speaker diarization or not",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default='./output',
+        help="Output files path",
+    )
+    parser.add_argument(
+        "--dest_text",
+        type=str,
+        default=None,
+        help="Destination text string for clipping",
+    )
+    parser.add_argument(
+        "--dest_spk",
+        type=str,
+        default=None,
+        help="Destination spk id for clipping",
+    )
+    parser.add_argument(
+        "--start_ost",
+        type=int,
+        default=0,
+        help="Offset time in ms at beginning for clipping"
+    )
+    parser.add_argument(
+        "--end_ost",
+        type=int,
+        default=0,
+        help="Offset time in ms at ending for clipping"
+    )
+    parser.add_argument(
+        "--output_file",
+        type=str,
+        default=None,
+        help="Output file path"
+    )
+    return parser
+
+
+def runner(stage, file, sd_switch, output_dir, dest_text, dest_spk, start_ost, end_ost, output_file, config=None):
+    audio_suffixs = ['wav']
+    video_suffixs = ['mp4']
+    if file[-3:] in audio_suffixs:
+        mode = 'audio'
+    elif file[-3:] in video_suffixs:
+        mode = 'video'
+    else:
+        logging.error("Unsupported file format: {}".format(file))
+    while output_dir.endswith('/'):
+        output_dir = output_dir[:-1]
+    if stage == 1:
+        from modelscope.pipelines import pipeline
+        from modelscope.utils.constant import Tasks
+        # initialize modelscope asr pipeline
+        logging.warning("Initializing modelscope asr pipeline.")
+        inference_pipeline = pipeline(
+            task=Tasks.auto_speech_recognition,
+            model='damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
+            vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
+            punc_model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch',
+            output_dir=output_dir,
+        )
+        sd_pipeline = pipeline(
+            task='speaker-diarization',
+            model='damo/speech_campplus_speaker-diarization_common',
+            model_revision='v1.0.0'
+        )
+        audio_clipper = VideoClipper(inference_pipeline, sd_pipeline)
+        if mode == 'audio':
+            logging.warning("Recognizing audio file: {}".format(file))
+            wav, sr = librosa.load(file, sr=16000)
+            res_text, res_srt, state = audio_clipper.recog((sr, wav), sd_switch)
+        if mode == 'video':
+            logging.warning("Recognizing video file: {}".format(file))
+            res_text, res_srt, state = audio_clipper.video_recog(file, sd_switch)
+        total_srt_file = output_dir + '/total.srt'
+        with open(total_srt_file, 'w') as fout:
+            fout.write(res_srt)
+            logging.warning("Write total subtitile to {}".format(total_srt_file))
+        write_state(output_dir, state)
+        logging.warning("Recognition successed. You can copy the text segment from below and use stage 2.")
+        print(res_text)
+    if stage == 2:
+        audio_clipper = VideoClipper(None)
+        if mode == 'audio':
+            state = load_state(output_dir)
+            wav, sr = librosa.load(file, sr=16000)
+            state['audio_input'] = (sr, wav)
+            (sr, audio), message, srt_clip = audio_clipper.clip(dest_text, start_ost, end_ost, state, dest_spk=dest_spk)
+            if output_file is None:
+                output_file = output_dir + '/result.wav'
+            clip_srt_file = output_file[:-3] + 'srt'
+            logging.warning(message)
+            sf.write(output_file, audio, 16000)
+            assert output_file.endswith('.wav'), "output_file must ends with '.wav'"
+            logging.warning("Save clipped wav file to {}".format(output_file))
+            with open(clip_srt_file, 'w') as fout:
+                fout.write(srt_clip)
+                logging.warning("Write clipped subtitile to {}".format(clip_srt_file))
+        if mode == 'video':
+            state = load_state(output_dir)
+            state['vedio_filename'] = file
+            if output_file is None:
+                state['clip_video_file'] = file[:-4] + '_clip.mp4'
+            else:
+                state['clip_video_file'] = output_file
+            clip_srt_file = state['clip_video_file'][:-3] + 'srt'
+            state['video'] = mpy.VideoFileClip(file)
+            clip_video_file, message, srt_clip = audio_clipper.video_clip(dest_text, start_ost, end_ost, state, dest_spk=dest_spk)
+            logging.warning("Clipping Log: {}".format(message))
+            logging.warning("Save clipped mp4 file to {}".format(clip_video_file))
+            with open(clip_srt_file, 'w') as fout:
+                fout.write(srt_clip)
+                logging.warning("Write clipped subtitile to {}".format(clip_srt_file))
+
+
+def main(cmd=None):
+    print(get_commandline_args(), file=sys.stderr)
+    parser = get_parser()
+    args = parser.parse_args(cmd)
+    kwargs = vars(args)
+    runner(**kwargs)
+
+
+if __name__ == '__main__':
+    main()

webui.py

@@ -6,6 +6,14 @@ import platform
 import psutil
 import signal
 
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+from videoclipper import VideoClipper
+import librosa
+import soundfile as sf
+import numpy as np
+import random
+
 warnings.filterwarnings("ignore")
 torch.manual_seed(233333)
 tmp = os.path.join(now_dir, "TEMP")
@@ -64,6 +72,60 @@ gpu_infos = []
 mem = []
 if_gpu_ok = False
 
+# 字幕语音切分
+inference_pipeline = pipeline(
+    task=Tasks.auto_speech_recognition,
+    model='damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
+    vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
+    punc_model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch',
+    ncpu=16,
+)
+sd_pipeline = pipeline(
+    task='speaker-diarization',
+    model='damo/speech_campplus_speaker-diarization_common',
+    model_revision='v1.0.0'
+)
+audio_clipper = VideoClipper(inference_pipeline, sd_pipeline)
+
+def audio_change(audio):
+
+    print(audio)
+
+    sf.write('./output_44100.wav', audio[1], audio[0], 'PCM_24')
+
+    y, sr = librosa.load('./output_44100.wav', sr=16000)
+
+    # sf.write('./output_16000.wav', y, sr, 'PCM_24')
+
+    # arr = np.array(y, dtype=np.int32)
+
+    # y, sr = librosa.load('./output_16000.wav', sr=16000)
+
+    audio_data = np.array(y)
+
+    print(y, sr)
+
+    return (16000,audio_data)
+
+def write_list(text,audio):
+    
+    random_number = random.randint(10000, 99999)
+
+    wav_name = f'./output/slicer_opt/sample_{random_number}.wav'
+
+    sf.write(wav_name, audio[1], audio[0], 'PCM_24')
+
+    text = text.replace("#",",")
+
+    with open("./output/asr_opt/slicer_opt.list","a",encoding="utf-8")as f:f.write(f"\n{wav_name}|slicer_opt|zh|{text}")
+
+def audio_recog(audio_input, sd_switch):
+    print(audio_input)
+    return audio_clipper.recog(audio_input, sd_switch)
+
+def audio_clip(dest_text, audio_spk_input, start_ost, end_ost, state):
+    return audio_clipper.clip(dest_text, start_ost, end_ost, state, dest_spk=audio_spk_input)
+
 # 判断是否有能用来训练和加速推理的N卡
 if torch.cuda.is_available() or ngpu != 0:
     for i in range(ngpu):
@@ -686,6 +748,41 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
             with gr.Row():
                 if_uvr5 = gr.Checkbox(label=i18n("是否开启UVR5-WebUI"),show_label=True)
                 uvr5_info = gr.Textbox(label=i18n("UVR5进程输出信息"))
+            gr.Markdown(value=i18n("0.5b-手动语义字幕语音切分工具"))
+            audio_state = gr.State()
+            with gr.Row():
+                with gr.Column():
+                    # oaudio_input = gr.Audio(label="🔊音频输入 44100hz Audio Input",type="filepath")
+                    # rec_audio = gr.Button("👂重新采样")
+                    audio_input = gr.Audio(label="🔊音频输入 16000hz Audio Input")
+                    audio_sd_switch = gr.Radio(["no", "yes"], label="👥是否区分说话人 Recognize Speakers", value='no')
+                    recog_button1 = gr.Button("👂识别 Recognize")
+                    audio_text_output = gr.Textbox(label="✏️识别结果 Recognition Result")
+                    audio_srt_output = gr.Textbox(label="📖SRT字幕内容 RST Subtitles")
+                with gr.Column():
+                    audio_text_input = gr.Textbox(label="✏️待裁剪文本 Text to Clip (多段文本使用'#'连接)")
+                    audio_spk_input = gr.Textbox(label="✏️待裁剪说话人 Speaker to Clip (多个说话人使用'#'连接)")
+                    with gr.Row():
+                        audio_start_ost = gr.Slider(minimum=-500, maximum=1000, value=0, step=50, label="⏪开始位置偏移 Start Offset (ms)")
+                        audio_end_ost = gr.Slider(minimum=-500, maximum=1000, value=0, step=50, label="⏩结束位置偏移 End Offset (ms)")
+                    with gr.Row():
+                        clip_button1 = gr.Button("✂️裁剪 Clip")
+                        write_button1 = gr.Button("写入转写文件")
+                    audio_output = gr.Audio(label="🔊裁剪结果 Audio Clipped")
+                    audio_mess_output = gr.Textbox(label="ℹ️裁剪信息 Clipping Log")
+                    audio_srt_clip_output = gr.Textbox(label="📖裁剪部分SRT字幕内容 Clipped RST Subtitles")
+
+            audio_input.change(inputs=audio_input, outputs=audio_input, fn=audio_change)
+
+            write_button1.click(write_list,[audio_text_input,audio_output],[])
+            
+            # rec_audio.click(re_write,[oaudio_input],[rec_audio])
+            recog_button1.click(audio_recog, 
+                            inputs=[audio_input, audio_sd_switch],
+                            outputs=[audio_text_output, audio_srt_output, audio_state])
+            clip_button1.click(audio_clip, 
+                            inputs=[audio_text_input, audio_spk_input, audio_start_ost, audio_end_ost, audio_state], 
+                            outputs=[audio_output, audio_mess_output, audio_srt_clip_output])
             gr.Markdown(value=i18n("0b-语音切分工具"))
             with gr.Row():
                 with gr.Row():