Merge pull request #130 from alibaba-damo-academy/dev_lzr

zhifu gao

2023-02-17 40b772101a379a9f2ee8dfb2d8278b1dad76420e

Merge pull request #130 from alibaba-damo-academy/dev_lzr

support paraformer-large-contextual with vad and punc model

 egs_modelscope/vad/speech_fsmn_vad_zh-cn-8k-common/infer.py

@@ -7,7 +7,7 @@
    inference_pipline = pipeline(
        task=Tasks.voice_activity_detection,
        model="damo/speech_fsmn_vad_zh-cn-8k-common",
        model_revision='v1.1.1',
        model_revision=None,
        output_dir='./output_dir',
        batch_size=1,
    )

 funasr/bin/asr_inference_paraformer_vad.py

@@ -167,6 +167,11 @@
        level=log_level,
        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
    )

    if param_dict is not None:
        hotword_list_or_file = param_dict.get('hotword')
    else:
        hotword_list_or_file = None
    
    if ngpu >= 1 and torch.cuda.is_available():
        device = "cuda"
@@ -206,6 +211,7 @@
        ngram_weight=ngram_weight,
        penalty=penalty,
        nbest=nbest,
        hotword_list_or_file=hotword_list_or_file,
    )
    speech2text = Speech2Text(**speech2text_kwargs)
    text2punc = None

 funasr/bin/asr_inference_paraformer_vad_punc.py

@@ -5,6 +5,10 @@
import logging
import sys
import time
import os
import codecs
import tempfile
import requests
from pathlib import Path
from typing import Optional
from typing import Sequence
@@ -41,7 +45,7 @@
from funasr.tasks.vad import VADTask
from funasr.utils.timestamp_tools import time_stamp_lfr6_pl
from funasr.bin.punctuation_infer import Text2Punc
from funasr.models.e2e_asr_paraformer import BiCifParaformer
from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer

header_colors = '\033[95m'
end_colors = '\033[0m'
@@ -79,6 +83,7 @@
            penalty: float = 0.0,
            nbest: int = 1,
            frontend_conf: dict = None,
            hotword_list_or_file: str = None,
            **kwargs,
    ):
        assert check_argument_types()
@@ -169,6 +174,58 @@
        self.asr_train_args = asr_train_args
        self.converter = converter
        self.tokenizer = tokenizer

        # 6. [Optional] Build hotword list from str, local file or url
        # for None
        if hotword_list_or_file is None:
            self.hotword_list = None
        # for text str input
        elif not os.path.exists(hotword_list_or_file) and not hotword_list_or_file.startswith('http'):
            logging.info("Attempting to parse hotwords as str...")
            self.hotword_list = []
            hotword_str_list = []
            for hw in hotword_list_or_file.strip().split():
                hotword_str_list.append(hw)
                self.hotword_list.append(self.converter.tokens2ids([i for i in hw]))
            self.hotword_list.append([self.asr_model.sos])
            hotword_str_list.append('<s>')
            logging.info("Hotword list: {}.".format(hotword_str_list))
        # for local txt inputs
        elif os.path.exists(hotword_list_or_file):
            logging.info("Attempting to parse hotwords from local txt...")
            self.hotword_list = []
            hotword_str_list = []
            with codecs.open(hotword_list_or_file, 'r') as fin:
                for line in fin.readlines():
                    hw = line.strip()
                    hotword_str_list.append(hw)
                    self.hotword_list.append(self.converter.tokens2ids([i for i in hw]))
                self.hotword_list.append([self.asr_model.sos])
                hotword_str_list.append('<s>')
            logging.info("Initialized hotword list from file: {}, hotword list: {}."
                .format(hotword_list_or_file, hotword_str_list))
        # for url, download and generate txt
        else:
            logging.info("Attempting to parse hotwords from url...")
            work_dir = tempfile.TemporaryDirectory().name
            if not os.path.exists(work_dir):
                os.makedirs(work_dir)
            text_file_path = os.path.join(work_dir, os.path.basename(hotword_list_or_file))
            local_file = requests.get(hotword_list_or_file)
            open(text_file_path, "wb").write(local_file.content)
            hotword_list_or_file = text_file_path
            self.hotword_list = []
            hotword_str_list = []
            with codecs.open(hotword_list_or_file, 'r') as fin:
                for line in fin.readlines():
                    hw = line.strip()
                    hotword_str_list.append(hw)
                    self.hotword_list.append(self.converter.tokens2ids([i for i in hw]))
                self.hotword_list.append([self.asr_model.sos])
                hotword_str_list.append('<s>')
            logging.info("Initialized hotword list from file: {}, hotword list: {}."
                .format(hotword_list_or_file, hotword_str_list))

        is_use_lm = lm_weight != 0.0 and lm_file is not None
        if (ctc_weight == 0.0 or asr_model.ctc == None) and not is_use_lm:
            beam_search = None
@@ -233,8 +290,15 @@
        pre_token_length = pre_token_length.round().long()
        if torch.max(pre_token_length) < 1:
            return []
        decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length)
        decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]

        if not isinstance(self.asr_model, ContextualParaformer):
            if self.hotword_list:
                logging.warning("Hotword is given but asr model is not a ContextualParaformer.")
            decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length)
            decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
        else:
            decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length, hw_list=self.hotword_list)
            decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]

        if isinstance(self.asr_model, BiCifParaformer):
            _, _, us_alphas, us_cif_peak = self.asr_model.calc_predictor_timestamp(enc, enc_len,
@@ -282,10 +346,11 @@
                else:
                    text = None


                timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time)
                results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor))

                if isinstance(self.asr_model, BiCifParaformer):
                    timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time)
                    results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor))
                else:
                    results.append((text, token, token_int, enc_len_batch_total, lfr_factor))

        # assert check_return_type(results)
        return results
@@ -512,6 +577,11 @@
        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
    )

    if param_dict is not None:
        hotword_list_or_file = param_dict.get('hotword')
    else:
        hotword_list_or_file = None

    if ngpu >= 1 and torch.cuda.is_available():
        device = "cuda"
    else:
@@ -550,6 +620,7 @@
        ngram_weight=ngram_weight,
        penalty=penalty,
        nbest=nbest,
        hotword_list_or_file=hotword_list_or_file,
    )
    speech2text = Speech2Text(**speech2text_kwargs)
    text2punc = None