Merge branch 'funasr1.0' of github.com:alibaba-damo-academy/FunASR into fun...

游雁

2024-01-10 d342c642fa7eae3d90cf543c28b153cf5dbd0722

Merge branch 'funasr1.0' of github.com:alibaba-damo-academy/FunASR into funasr1.0
add

 runtime/python/onnxruntime/demo_contextual_paraformer.py

@@ -1,7 +1,7 @@
from funasr_onnx import ContextualParaformer
from pathlib import Path

model_dir = "./export/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404"
model_dir = "../export/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404" # your export dir
model = ContextualParaformer(model_dir, batch_size=1)

wav_path = ['{}/.cache/modelscope/hub/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/example/asr_example.wav'.format(Path.home())]

 runtime/python/onnxruntime/funasr_onnx/paraformer_bin.py

@@ -7,7 +7,6 @@
from typing import List, Union, Tuple

import copy
import torch
import librosa
import numpy as np

@@ -18,7 +17,7 @@
                                      sentence_postprocess_sentencepiece)
from .utils.frontend import WavFrontend
from .utils.timestamp_utils import time_stamp_lfr6_onnx
from .utils.utils import pad_list, make_pad_mask
from .utils.utils import pad_list

logging = get_logger()

@@ -309,7 +308,7 @@
        # index from bias_embed
        bias_embed = bias_embed.transpose(1, 0, 2)
        _ind = np.arange(0, len(hotwords)).tolist()
        bias_embed = bias_embed[_ind, hotwords_length.cpu().numpy().tolist()]
        bias_embed = bias_embed[_ind, hotwords_length.tolist()]
        waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
        waveform_nums = len(waveform_list)
        asr_res = []
@@ -336,7 +335,7 @@
        hotwords = hotwords.split(" ")
        hotwords_length = [len(i) - 1 for i in hotwords]
        hotwords_length.append(0)
        hotwords_length = torch.Tensor(hotwords_length).to(torch.int32)
        hotwords_length = np.array(hotwords_length)
        # hotwords.append('<s>')
        def word_map(word):
            hotwords = []
@@ -346,11 +345,12 @@
                    logging.warning("oov character {} found in hotword {}, replaced by <unk>".format(c, word))
                else:
                    hotwords.append(self.vocab[c])
            return torch.tensor(hotwords)
            return np.array(hotwords)
        hotword_int = [word_map(i) for i in hotwords]
        # import pdb; pdb.set_trace()
        hotword_int.append(torch.tensor([1]))
        hotword_int.append(np.array([1]))
        hotwords = pad_list(hotword_int, pad_value=0, max_len=10)
        # import pdb; pdb.set_trace()
        return hotwords, hotwords_length

    def bb_infer(self, feats: np.ndarray,
@@ -359,7 +359,7 @@
        return outputs

    def eb_infer(self, hotwords, hotwords_length):
        outputs = self.ort_infer_eb([hotwords.to(torch.int32).numpy(), hotwords_length.to(torch.int32).numpy()])
        outputs = self.ort_infer_eb([hotwords.astype(np.int32), hotwords_length.astype(np.int32)])
        return outputs

    def decode(self, am_scores: np.ndarray, token_nums: int) -> List[str]:

 runtime/python/onnxruntime/funasr_onnx/utils/utils.py

@@ -2,12 +2,10 @@

import functools
import logging
import pickle
from pathlib import Path
from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union

import re
import torch
import numpy as np
import yaml
try:
@@ -27,14 +25,15 @@
    n_batch = len(xs)
    if max_len is None:
        max_len = max(x.size(0) for x in xs)
    pad = xs[0].new(n_batch, max_len, *xs[0].size()[1:]).fill_(pad_value)

    # pad = xs[0].new(n_batch, max_len, *xs[0].size()[1:]).fill_(pad_value)
    # numpy format
    pad = (np.zeros((n_batch, max_len)) + pad_value).astype(np.int32)
    for i in range(n_batch):
        pad[i, : xs[i].size(0)] = xs[i]
        pad[i, : xs[i].shape[0]] = xs[i]

    return pad


'''
def make_pad_mask(lengths, xs=None, length_dim=-1, maxlen=None):
    if length_dim == 0:
        raise ValueError("length_dim cannot be 0: {}".format(length_dim))
@@ -67,7 +66,7 @@
        )
        mask = mask[ind].expand_as(xs).to(xs.device)
    return mask

'''

class TokenIDConverter():
    def __init__(self, token_list: Union[List, str],