update cam++ for embed extract

shixian.shi

2024-01-10 668b830cb2a8f69c1cfb131ec9542d27f91b7283

update cam++ for embed extract

 examples/industrial_data_pretraining/spk_verification/demo.py

New file
@@ -0,0 +1,11 @@
#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

from funasr import AutoModel

model = AutoModel(model="/Users/shixian/code/modelscope_models/speech_campplus_sv_zh-cn_16k-common")

res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
print(res)

 funasr/bin/inference.py

@@ -159,6 +159,9 @@
            tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
            kwargs["tokenizer"] = tokenizer
            kwargs["token_list"] = tokenizer.token_list
            vocab_size = len(tokenizer.token_list)
        else:
            vocab_size = -1
        
        # build frontend
        frontend = kwargs.get("frontend", None)
@@ -170,8 +173,7 @@
        
        # build model
        model_class = tables.model_classes.get(kwargs["model"].lower())
        model = model_class(**kwargs, **kwargs["model_conf"],
                            vocab_size=len(tokenizer.token_list) if tokenizer is not None else -1)
        model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
        model.eval()
        model.to(device)
        

 funasr/models/campplus/__init__.py

@@ -1 +0,0 @@
from .campplus import CAMPPlus

 funasr/models/campplus/components.py

File was renamed from funasr/models/campplus/layers.py
@@ -7,6 +7,82 @@
from torch import nn


class BasicResBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicResBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes,
                               planes,
                               kernel_size=3,
                               stride=(stride, 1),
                               padding=1,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes,
                               planes,
                               kernel_size=3,
                               stride=1,
                               padding=1,
                               bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes,
                          self.expansion * planes,
                          kernel_size=1,
                          stride=(stride, 1),
                          bias=False),
                nn.BatchNorm2d(self.expansion * planes))

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class FCM(nn.Module):
    def __init__(self,
                 block=BasicResBlock,
                 num_blocks=[2, 2],
                 m_channels=32,
                 feat_dim=80):
        super(FCM, self).__init__()
        self.in_planes = m_channels
        self.conv1 = nn.Conv2d(1, m_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(m_channels)

        self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
        self.layer2 = self._make_layer(block, m_channels, num_blocks[0], stride=2)

        self.conv2 = nn.Conv2d(m_channels, m_channels, kernel_size=3, stride=(2, 1), padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(m_channels)
        self.out_channels = m_channels * (feat_dim // 8)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        x = x.unsqueeze(1)
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = F.relu(self.bn2(self.conv2(out)))

        shape = out.shape
        out = out.reshape(shape[0], shape[1] * shape[2], shape[3])
        return out


def get_nonlinear(config_str, channels):
    nonlinear = nn.Sequential()
    for name in config_str.split('-'):
@@ -216,39 +292,3 @@
        return x


class BasicResBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicResBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes,
                               planes,
                               kernel_size=3,
                               stride=(stride, 1),
                               padding=1,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes,
                               planes,
                               kernel_size=3,
                               stride=1,
                               padding=1,
                               bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes,
                          self.expansion * planes,
                          kernel_size=1,
                          stride=(stride, 1),
                          bias=False),
                nn.BatchNorm2d(self.expansion * planes))

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out

 funasr/models/campplus/model.py

File was renamed from funasr/models/campplus/campplus.py
@@ -1,54 +1,24 @@
# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)

import os
import time
import torch
import logging
import numpy as np
import torch.nn as nn
from collections import OrderedDict
from typing import Union, Dict, List, Tuple, Optional

import torch.nn.functional as F
from torch import nn
from funasr.utils.load_utils import load_audio_text_image_video
from funasr.utils.datadir_writer import DatadirWriter
from funasr.register import tables
from funasr.models.campplus.components import DenseLayer, StatsPool, TDNNLayer, CAMDenseTDNNBlock, TransitLayer, \
    BasicResBlock, get_nonlinear, FCM
from funasr.models.campplus.utils import extract_feature


from funasr.models.campplus.layers import DenseLayer, StatsPool, TDNNLayer, CAMDenseTDNNBlock, TransitLayer, \
    BasicResBlock, get_nonlinear


class FCM(nn.Module):
    def __init__(self,
                 block=BasicResBlock,
                 num_blocks=[2, 2],
                 m_channels=32,
                 feat_dim=80):
        super(FCM, self).__init__()
        self.in_planes = m_channels
        self.conv1 = nn.Conv2d(1, m_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(m_channels)

        self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
        self.layer2 = self._make_layer(block, m_channels, num_blocks[0], stride=2)

        self.conv2 = nn.Conv2d(m_channels, m_channels, kernel_size=3, stride=(2, 1), padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(m_channels)
        self.out_channels = m_channels * (feat_dim // 8)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        x = x.unsqueeze(1)
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = F.relu(self.bn2(self.conv2(out)))

        shape = out.shape
        out = out.reshape(shape[0], shape[1] * shape[2], shape[3])
        return out


@tables.register("model_classes", "CAMPPlus")
class CAMPPlus(nn.Module):
    def __init__(self,
                 feat_dim=80,
@@ -58,8 +28,9 @@
                 init_channels=128,
                 config_str='batchnorm-relu',
                 memory_efficient=True,
                 output_level='segment'):
        super(CAMPPlus, self).__init__()
                 output_level='segment',
                 **kwargs,):
        super().__init__()

        self.head = FCM(feat_dim=feat_dim)
        channels = self.head.out_channels
@@ -123,3 +94,28 @@
        if self.output_level == 'frame':
            x = x.transpose(1, 2)
        return x

    def generate(self,
                 data_in,
                 data_lengths=None,
                 key: list=None,
                 tokenizer=None,
                 frontend=None,
                 **kwargs,
                 ):
        # extract fbank feats
        meta_data = {}
        time1 = time.perf_counter()
        audio_sample_list = load_audio_text_image_video(data_in, fs=16000, audio_fs=kwargs.get("fs", 16000), data_type="sound")
        time2 = time.perf_counter()
        meta_data["load_data"] = f"{time2 - time1:0.3f}"
        speech, speech_lengths = extract_feature(audio_sample_list)
        time3 = time.perf_counter()
        meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
        meta_data["batch_data_time"] = np.array(speech_lengths).sum().item() / 16000.0
        # import pdb; pdb.set_trace()
        results = []
        embeddings = self.forward(speech)
        for embedding in embeddings:
            results.append({"spk_embedding":embedding})
        return results, meta_data

 funasr/models/campplus/template.yaml

New file
@@ -0,0 +1,23 @@
# This is an example that demonstrates how to configure a model file.
# You can modify the configuration according to your own requirements.

# to print the register_table:
# from funasr.register import tables
# tables.print()

# network architecture
model: CAMPPlus
model_conf:
    feat_dim: 80
    embedding_size: 192
    growth_rate: 32
    bn_size: 4
    init_channels: 128
    config_str: 'batchnorm-relu'
    memory_efficient: True
    output_level: 'segment'

# frontend related
frontend: WavFrontend
frontend_conf:
    fs: 16000

 funasr/models/campplus/utils.py

New file
@@ -0,0 +1,533 @@
# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)

import io
from typing import Union

import librosa as sf
import numpy as np
import torch
import torch.nn.functional as F
import torchaudio.compliance.kaldi as Kaldi
from torch import nn

import contextlib
import os
import tempfile
from abc import ABCMeta, abstractmethod
from pathlib import Path
from typing import Generator, Union

import requests


def check_audio_list(audio: list):
    audio_dur = 0
    for i in range(len(audio)):
        seg = audio[i]
        assert seg[1] >= seg[0], 'modelscope error: Wrong time stamps.'
        assert isinstance(seg[2], np.ndarray), 'modelscope error: Wrong data type.'
        assert int(seg[1] * 16000) - int(
            seg[0] * 16000
        ) == seg[2].shape[
            0], 'modelscope error: audio data in list is inconsistent with time length.'
        if i > 0:
            assert seg[0] >= audio[
                i - 1][1], 'modelscope error: Wrong time stamps.'
        audio_dur += seg[1] - seg[0]
    return audio_dur
    # assert audio_dur > 5, 'modelscope error: The effective audio duration is too short.'


def sv_preprocess(inputs: Union[np.ndarray, list]):
    output = []
    for i in range(len(inputs)):
        if isinstance(inputs[i], str):
            file_bytes = File.read(inputs[i])
            data, fs = sf.load(io.BytesIO(file_bytes), dtype='float32')
            if len(data.shape) == 2:
                data = data[:, 0]
            data = torch.from_numpy(data).unsqueeze(0)
            data = data.squeeze(0)
        elif isinstance(inputs[i], np.ndarray):
            assert len(
                inputs[i].shape
            ) == 1, 'modelscope error: Input array should be [N, T]'
            data = inputs[i]
            if data.dtype in ['int16', 'int32', 'int64']:
                data = (data / (1 << 15)).astype('float32')
            else:
                data = data.astype('float32')
            data = torch.from_numpy(data)
        else:
            raise ValueError(
                'modelscope error: The input type is restricted to audio address and nump array.'
            )
        output.append(data)
    return output


def sv_chunk(vad_segments: list, fs = 16000) -> list:
    config = {
            'seg_dur': 1.5,
            'seg_shift': 0.75,
        }
    def seg_chunk(seg_data):
        seg_st = seg_data[0]
        data = seg_data[2]
        chunk_len = int(config['seg_dur'] * fs)
        chunk_shift = int(config['seg_shift'] * fs)
        last_chunk_ed = 0
        seg_res = []
        for chunk_st in range(0, data.shape[0], chunk_shift):
            chunk_ed = min(chunk_st + chunk_len, data.shape[0])
            if chunk_ed <= last_chunk_ed:
                break
            last_chunk_ed = chunk_ed
            chunk_st = max(0, chunk_ed - chunk_len)
            chunk_data = data[chunk_st:chunk_ed]
            if chunk_data.shape[0] < chunk_len:
                chunk_data = np.pad(chunk_data,
                                    (0, chunk_len - chunk_data.shape[0]),
                                    'constant')
            seg_res.append([
                chunk_st / fs + seg_st, chunk_ed / fs + seg_st,
                chunk_data
            ])
        return seg_res

    segs = []
    for i, s in enumerate(vad_segments):
        segs.extend(seg_chunk(s))

    return segs


def extract_feature(audio):
    features = []
    feature_lengths = []
    for au in audio:
        feature = Kaldi.fbank(
            au.unsqueeze(0), num_mel_bins=80)
        feature = feature - feature.mean(dim=0, keepdim=True)
        features.append(feature.unsqueeze(0))
        feature_lengths.append(au.shape[0])
    features = torch.cat(features)
    return features, feature_lengths


def postprocess(segments: list, vad_segments: list,
                labels: np.ndarray, embeddings: np.ndarray) -> list:
    assert len(segments) == len(labels)
    labels = correct_labels(labels)
    distribute_res = []
    for i in range(len(segments)):
        distribute_res.append([segments[i][0], segments[i][1], labels[i]])
    # merge the same speakers chronologically
    distribute_res = merge_seque(distribute_res)

    # accquire speaker center
    spk_embs = []
    for i in range(labels.max() + 1):
        spk_emb = embeddings[labels == i].mean(0)
        spk_embs.append(spk_emb)
    spk_embs = np.stack(spk_embs)

    def is_overlapped(t1, t2):
        if t1 > t2 + 1e-4:
            return True
        return False

    # distribute the overlap region
    for i in range(1, len(distribute_res)):
        if is_overlapped(distribute_res[i - 1][1], distribute_res[i][0]):
            p = (distribute_res[i][0] + distribute_res[i - 1][1]) / 2
            distribute_res[i][0] = p
            distribute_res[i - 1][1] = p

    # smooth the result
    distribute_res = smooth(distribute_res)

    return distribute_res


def correct_labels(labels):
    labels_id = 0
    id2id = {}
    new_labels = []
    for i in labels:
        if i not in id2id:
            id2id[i] = labels_id
            labels_id += 1
        new_labels.append(id2id[i])
    return np.array(new_labels)

def merge_seque(distribute_res):
    res = [distribute_res[0]]
    for i in range(1, len(distribute_res)):
        if distribute_res[i][2] != res[-1][2] or distribute_res[i][
                0] > res[-1][1]:
            res.append(distribute_res[i])
        else:
            res[-1][1] = distribute_res[i][1]
    return res

def smooth(res, mindur=1):
    # short segments are assigned to nearest speakers.
    for i in range(len(res)):
        res[i][0] = round(res[i][0], 2)
        res[i][1] = round(res[i][1], 2)
        if res[i][1] - res[i][0] < mindur:
            if i == 0:
                res[i][2] = res[i + 1][2]
            elif i == len(res) - 1:
                res[i][2] = res[i - 1][2]
            elif res[i][0] - res[i - 1][1] <= res[i + 1][0] - res[i][1]:
                res[i][2] = res[i - 1][2]
            else:
                res[i][2] = res[i + 1][2]
    # merge the speakers
    res = merge_seque(res)

    return res


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




class Storage(metaclass=ABCMeta):
    """Abstract class of storage.

    All backends need to implement two apis: ``read()`` and ``read_text()``.
    ``read()`` reads the file as a byte stream and ``read_text()`` reads
    the file as texts.
    """

    @abstractmethod
    def read(self, filepath: str):
        pass

    @abstractmethod
    def read_text(self, filepath: str):
        pass

    @abstractmethod
    def write(self, obj: bytes, filepath: Union[str, Path]) -> None:
        pass

    @abstractmethod
    def write_text(self,
                   obj: str,
                   filepath: Union[str, Path],
                   encoding: str = 'utf-8') -> None:
        pass


class LocalStorage(Storage):
    """Local hard disk storage"""

    def read(self, filepath: Union[str, Path]) -> bytes:
        """Read data from a given ``filepath`` with 'rb' mode.

        Args:
            filepath (str or Path): Path to read data.

        Returns:
            bytes: Expected bytes object.
        """
        with open(filepath, 'rb') as f:
            content = f.read()
        return content

    def read_text(self,
                  filepath: Union[str, Path],
                  encoding: str = 'utf-8') -> str:
        """Read data from a given ``filepath`` with 'r' mode.

        Args:
            filepath (str or Path): Path to read data.
            encoding (str): The encoding format used to open the ``filepath``.
                Default: 'utf-8'.

        Returns:
            str: Expected text reading from ``filepath``.
        """
        with open(filepath, 'r', encoding=encoding) as f:
            value_buf = f.read()
        return value_buf

    def write(self, obj: bytes, filepath: Union[str, Path]) -> None:
        """Write data to a given ``filepath`` with 'wb' mode.

        Note:
            ``write`` will create a directory if the directory of ``filepath``
            does not exist.

        Args:
            obj (bytes): Data to be written.
            filepath (str or Path): Path to write data.
        """
        dirname = os.path.dirname(filepath)
        if dirname and not os.path.exists(dirname):
            os.makedirs(dirname, exist_ok=True)

        with open(filepath, 'wb') as f:
            f.write(obj)

    def write_text(self,
                   obj: str,
                   filepath: Union[str, Path],
                   encoding: str = 'utf-8') -> None:
        """Write data to a given ``filepath`` with 'w' mode.

        Note:
            ``write_text`` will create a directory if the directory of
            ``filepath`` does not exist.

        Args:
            obj (str): Data to be written.
            filepath (str or Path): Path to write data.
            encoding (str): The encoding format used to open the ``filepath``.
                Default: 'utf-8'.
        """
        dirname = os.path.dirname(filepath)
        if dirname and not os.path.exists(dirname):
            os.makedirs(dirname, exist_ok=True)

        with open(filepath, 'w', encoding=encoding) as f:
            f.write(obj)

    @contextlib.contextmanager
    def as_local_path(
            self,
            filepath: Union[str,
                            Path]) -> Generator[Union[str, Path], None, None]:
        """Only for unified API and do nothing."""
        yield filepath


class HTTPStorage(Storage):
    """HTTP and HTTPS storage."""

    def read(self, url):
        # TODO @wenmeng.zwm add progress bar if file is too large
        r = requests.get(url)
        r.raise_for_status()
        return r.content

    def read_text(self, url):
        r = requests.get(url)
        r.raise_for_status()
        return r.text

    @contextlib.contextmanager
    def as_local_path(
            self, filepath: str) -> Generator[Union[str, Path], None, None]:
        """Download a file from ``filepath``.

        ``as_local_path`` is decorated by :meth:`contextlib.contextmanager`. It
        can be called with ``with`` statement, and when exists from the
        ``with`` statement, the temporary path will be released.

        Args:
            filepath (str): Download a file from ``filepath``.

        Examples:
            >>> storage = HTTPStorage()
            >>> # After existing from the ``with`` clause,
            >>> # the path will be removed
            >>> with storage.get_local_path('http://path/to/file') as path:
            ...     # do something here
        """
        try:
            f = tempfile.NamedTemporaryFile(delete=False)
            f.write(self.read(filepath))
            f.close()
            yield f.name
        finally:
            os.remove(f.name)

    def write(self, obj: bytes, url: Union[str, Path]) -> None:
        raise NotImplementedError('write is not supported by HTTP Storage')

    def write_text(self,
                   obj: str,
                   url: Union[str, Path],
                   encoding: str = 'utf-8') -> None:
        raise NotImplementedError(
            'write_text is not supported by HTTP Storage')


class OSSStorage(Storage):
    """OSS storage."""

    def __init__(self, oss_config_file=None):
        # read from config file or env var
        raise NotImplementedError(
            'OSSStorage.__init__ to be implemented in the future')

    def read(self, filepath):
        raise NotImplementedError(
            'OSSStorage.read to be implemented in the future')

    def read_text(self, filepath, encoding='utf-8'):
        raise NotImplementedError(
            'OSSStorage.read_text to be implemented in the future')

    @contextlib.contextmanager
    def as_local_path(
            self, filepath: str) -> Generator[Union[str, Path], None, None]:
        """Download a file from ``filepath``.

        ``as_local_path`` is decorated by :meth:`contextlib.contextmanager`. It
        can be called with ``with`` statement, and when exists from the
        ``with`` statement, the temporary path will be released.

        Args:
            filepath (str): Download a file from ``filepath``.

        Examples:
            >>> storage = OSSStorage()
            >>> # After existing from the ``with`` clause,
            >>> # the path will be removed
            >>> with storage.get_local_path('http://path/to/file') as path:
            ...     # do something here
        """
        try:
            f = tempfile.NamedTemporaryFile(delete=False)
            f.write(self.read(filepath))
            f.close()
            yield f.name
        finally:
            os.remove(f.name)

    def write(self, obj: bytes, filepath: Union[str, Path]) -> None:
        raise NotImplementedError(
            'OSSStorage.write to be implemented in the future')

    def write_text(self,
                   obj: str,
                   filepath: Union[str, Path],
                   encoding: str = 'utf-8') -> None:
        raise NotImplementedError(
            'OSSStorage.write_text to be implemented in the future')


G_STORAGES = {}


class File(object):
    _prefix_to_storage: dict = {
        'oss': OSSStorage,
        'http': HTTPStorage,
        'https': HTTPStorage,
        'local': LocalStorage,
    }

    @staticmethod
    def _get_storage(uri):
        assert isinstance(uri,
                          str), f'uri should be str type, but got {type(uri)}'

        if '://' not in uri:
            # local path
            storage_type = 'local'
        else:
            prefix, _ = uri.split('://')
            storage_type = prefix

        assert storage_type in File._prefix_to_storage, \
            f'Unsupported uri {uri}, valid prefixs: '\
            f'{list(File._prefix_to_storage.keys())}'

        if storage_type not in G_STORAGES:
            G_STORAGES[storage_type] = File._prefix_to_storage[storage_type]()

        return G_STORAGES[storage_type]

    @staticmethod
    def read(uri: str) -> bytes:
        """Read data from a given ``filepath`` with 'rb' mode.

        Args:
            filepath (str or Path): Path to read data.

        Returns:
            bytes: Expected bytes object.
        """
        storage = File._get_storage(uri)
        return storage.read(uri)

    @staticmethod
    def read_text(uri: Union[str, Path], encoding: str = 'utf-8') -> str:
        """Read data from a given ``filepath`` with 'r' mode.

        Args:
            filepath (str or Path): Path to read data.
            encoding (str): The encoding format used to open the ``filepath``.
                Default: 'utf-8'.

        Returns:
            str: Expected text reading from ``filepath``.
        """
        storage = File._get_storage(uri)
        return storage.read_text(uri)

    @staticmethod
    def write(obj: bytes, uri: Union[str, Path]) -> None:
        """Write data to a given ``filepath`` with 'wb' mode.

        Note:
            ``write`` will create a directory if the directory of ``filepath``
            does not exist.

        Args:
            obj (bytes): Data to be written.
            filepath (str or Path): Path to write data.
        """
        storage = File._get_storage(uri)
        return storage.write(obj, uri)

    @staticmethod
    def write_text(obj: str, uri: str, encoding: str = 'utf-8') -> None:
        """Write data to a given ``filepath`` with 'w' mode.

        Note:
            ``write_text`` will create a directory if the directory of
            ``filepath`` does not exist.

        Args:
            obj (str): Data to be written.
            filepath (str or Path): Path to write data.
            encoding (str): The encoding format used to open the ``filepath``.
                Default: 'utf-8'.
        """
        storage = File._get_storage(uri)
        return storage.write_text(obj, uri)

    @contextlib.contextmanager
    def as_local_path(uri: str) -> Generator[Union[str, Path], None, None]:
        """Only for unified API and do nothing."""
        storage = File._get_storage(uri)
        with storage.as_local_path(uri) as local_path:
            yield local_path

 funasr/models/paraformer/model.py

@@ -447,7 +447,6 @@
             frontend=None,
             **kwargs,
             ):
		
        # init beamsearch
        is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
        is_use_lm = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
@@ -475,7 +474,6 @@
            meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
            
        speech.to(device=kwargs["device"]), speech_lengths.to(device=kwargs["device"])

        # Encoder
        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
        if isinstance(encoder_out, tuple):