mirror of
https://github.com/modelscope/FunASR.git
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feat: add ERes2NetV2 speaker model with full AutoModel integration
Integrates ERes2NetV2 from 3D-Speaker into FunASR as a speaker
verification/diarization model. Can be used standalone or as spk_model
combined with VAD + ASR for speaker-attributed transcription.
Usage (standalone speaker embedding):
model = AutoModel(model="iic/speech_eres2netv2_sv_zh-cn_16k-common")
res = model.generate(input="audio.wav")
embedding = res[0]["spk_embedding"] # shape: [1, 192]
Usage (with ASR + speaker diarization):
model = AutoModel(
model="paraformer-zh",
vad_model="fsmn-vad",
spk_model="iic/speech_eres2netv2_sv_zh-cn_16k-common",
)
res = model.generate(input="audio.wav")
Tested end-to-end: ModelScope download + embedding extraction verified.
New files:
- funasr/models/eres2net/eres2netv2.py (network architecture)
- funasr/models/eres2net/model.py (FunASR wrapper with inference)
- tests_models/test_eres2netv2.py
- examples/industrial_data_pretraining/eres2netv2_sv/demo.py
- examples/industrial_data_pretraining/eres2netv2_sv/demo_sv.py
This commit is contained in:
游雁
@@ -123,6 +123,7 @@ FunASR has open-sourced a large number of pre-trained models on industrial data.
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| fsmn-kws <br> ( [⭐](https://modelscope.cn/models/iic/speech_charctc_kws_phone-xiaoyun/summary) ) | keyword spotting，streaming | 5000 hours, Mandarin | 0.7M |
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| fa-zh <br> ( [⭐](https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary) [🤗](https://huggingface.co/funasr/fa-zh) ) | timestamp prediction | 5000 hours, Mandarin | 38M |
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| cam++ <br> ( [⭐](https://modelscope.cn/models/iic/speech_campplus_sv_zh-cn_16k-common/summary) [🤗](https://huggingface.co/funasr/campplus) ) | speaker verification/diarization | 5000 hours | 7.2M |
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| ERes2NetV2 <br> ([⭐](examples/industrial_data_pretraining/eres2netv2_sv/demo.py) [⭐](https://modelscope.cn/models/iic/speech_eres2netv2_sv_zh-cn_16k-common/summary) ) | speaker verification/diarization | 5000 hours | 17.8M |
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| Whisper-large-v3 <br> ([⭐](https://www.modelscope.cn/models/iic/Whisper-large-v3/summary) [🍀](https://github.com/openai/whisper) ) | speech recognition, with timestamps, non-streaming | multilingual | 1550 M |
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| Whisper-large-v3-turbo <br> ([⭐](https://www.modelscope.cn/models/iic/Whisper-large-v3-turbo/summary) [🍀](https://github.com/openai/whisper) ) | speech recognition, with timestamps, non-streaming | multilingual | 809 M |
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| Qwen-Audio <br> ([⭐](examples/industrial_data_pretraining/qwen_audio/demo.py) [🤗](https://huggingface.co/Qwen/Qwen-Audio) ) | audio-text multimodal models (pretraining) | multilingual | 8B |
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@@ -123,6 +123,7 @@ FunASR 开源了大量在工业数据上预训练模型，您可以在[模型许
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| fsmn-kws <br> ( [⭐](https://modelscope.cn/models/iic/speech_charctc_kws_phone-xiaoyun/summary) ) | 语音唤醒，实时 | 5000 小时，中文 | 0.7M |
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| fa-zh <br> ( [⭐](https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary) [🤗](https://huggingface.co/funasr/fa-zh) ) | 字级别时间戳预测 | 50000 小时，中文 | 38M |
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| cam++ <br> ( [⭐](https://modelscope.cn/models/iic/speech_campplus_sv_zh-cn_16k-common/summary) [🤗](https://huggingface.co/funasr/campplus) ) | 说话人确认/分割 | 5000 小时 | 7.2M |
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| ERes2NetV2 <br> ([⭐](examples/industrial_data_pretraining/eres2netv2_sv/demo.py) [⭐](https://modelscope.cn/models/iic/speech_eres2netv2_sv_zh-cn_16k-common/summary) ) | 说话人确认/分割 | 5000小时 | 17.8M |
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| Whisper-large-v3 <br> ([⭐](https://www.modelscope.cn/models/iic/Whisper-large-v3/summary) [🍀](https://github.com/openai/whisper) ) | 语音识别，带时间戳输出，非实时 | 多语言 | 1550 M |
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| Whisper-large-v3-turbo <br> ([⭐](https://www.modelscope.cn/models/iic/Whisper-large-v3-turbo/summary) [🍀](https://github.com/openai/whisper) ) | 语音识别，带时间戳输出，非实时 | 多语言 | 809 M |
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| Qwen-Audio <br> ([⭐](examples/industrial_data_pretraining/qwen_audio/demo.py) [🤗](https://huggingface.co/Qwen/Qwen-Audio) ) | 音频文本多模态大模型（预训练） | 多语言 | 8B |
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@@ -0,0 +1,24 @@
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#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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from funasr import AutoModel
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# ASR with speaker diarization using ERes2NetV2
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model = AutoModel(
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model="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
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vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
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vad_kwargs={"max_single_segment_time": 60000},
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punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
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spk_model="iic/speech_eres2netv2_sv_zh-cn_16k-common",
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device="cuda:0",
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)
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res = model.generate(
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input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
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batch_size_s=300,
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)
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for sentence in res:
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print(f"[Speaker {sentence.get('spk', '?')}] {sentence['text']}")
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@@ -0,0 +1,20 @@
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#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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from funasr import AutoModel
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# Standalone speaker verification / embedding extraction
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model = AutoModel(
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model="iic/speech_eres2netv2_sv_zh-cn_16k-common",
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device="cuda:0",
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)
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# Extract speaker embedding
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res = model.generate(
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input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
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)
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embedding = res[0]["spk_embedding"]
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print(f"Speaker embedding shape: {embedding.shape}")
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@@ -0,0 +1,226 @@
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# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import funasr.models.sond.pooling.pooling_layers as pooling_layers
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from funasr.models.eres2net.fusion import AFF
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class ReLU(nn.Hardtanh):
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def __init__(self, inplace=False):
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super(ReLU, self).__init__(0, 20, inplace)
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def __repr__(self):
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inplace_str = "inplace" if self.inplace else ""
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return self.__class__.__name__ + " (" + inplace_str + ")"
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class BasicBlockERes2NetV2(nn.Module):
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def __init__(self, in_planes, planes, stride=1, baseWidth=26, scale=2, expansion=2):
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super(BasicBlockERes2NetV2, self).__init__()
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width = int(math.floor(planes * (baseWidth / 64.0)))
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self.conv1 = nn.Conv2d(in_planes, width * scale, kernel_size=1, stride=stride, bias=False)
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self.bn1 = nn.BatchNorm2d(width * scale)
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self.nums = scale
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self.expansion = expansion
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convs = []
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bns = []
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for i in range(self.nums):
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convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
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bns.append(nn.BatchNorm2d(width))
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self.convs = nn.ModuleList(convs)
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self.bns = nn.ModuleList(bns)
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self.relu = ReLU(inplace=True)
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self.conv3 = nn.Conv2d(width * scale, planes * self.expansion, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes * self.expansion)
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self.shortcut = nn.Sequential()
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if stride != 1 or in_planes != self.expansion * planes:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(self.expansion * planes),
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)
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self.stride = stride
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self.width = width
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self.scale = scale
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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spx = torch.split(out, self.width, 1)
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for i in range(self.nums):
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if i == 0:
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sp = spx[i]
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else:
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sp = sp + spx[i]
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sp = self.convs[i](sp)
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sp = self.relu(self.bns[i](sp))
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if i == 0:
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out = sp
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else:
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out = torch.cat((out, sp), 1)
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out = self.conv3(out)
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out = self.bn3(out)
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residual = self.shortcut(x)
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out += residual
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out = self.relu(out)
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return out
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class BasicBlockERes2NetV2AFF(nn.Module):
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def __init__(self, in_planes, planes, stride=1, baseWidth=26, scale=2, expansion=2):
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super(BasicBlockERes2NetV2AFF, self).__init__()
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width = int(math.floor(planes * (baseWidth / 64.0)))
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self.conv1 = nn.Conv2d(in_planes, width * scale, kernel_size=1, stride=stride, bias=False)
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self.bn1 = nn.BatchNorm2d(width * scale)
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self.nums = scale
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self.expansion = expansion
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convs = []
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fuse_models = []
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bns = []
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for i in range(self.nums):
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convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
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bns.append(nn.BatchNorm2d(width))
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for j in range(self.nums - 1):
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fuse_models.append(AFF(channels=width, r=4))
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self.convs = nn.ModuleList(convs)
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self.bns = nn.ModuleList(bns)
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self.fuse_models = nn.ModuleList(fuse_models)
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self.relu = ReLU(inplace=True)
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self.conv3 = nn.Conv2d(width * scale, planes * self.expansion, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes * self.expansion)
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self.shortcut = nn.Sequential()
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if stride != 1 or in_planes != self.expansion * planes:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(self.expansion * planes),
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)
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self.stride = stride
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self.width = width
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self.scale = scale
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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spx = torch.split(out, self.width, 1)
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for i in range(self.nums):
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if i == 0:
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sp = spx[i]
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else:
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sp = self.fuse_models[i - 1](sp, spx[i])
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sp = self.convs[i](sp)
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sp = self.relu(self.bns[i](sp))
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if i == 0:
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out = sp
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else:
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out = torch.cat((out, sp), 1)
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out = self.conv3(out)
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out = self.bn3(out)
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residual = self.shortcut(x)
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out += residual
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out = self.relu(out)
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return out
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class ERes2NetV2(nn.Module):
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def __init__(
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self,
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block=BasicBlockERes2NetV2,
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block_fuse=BasicBlockERes2NetV2AFF,
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num_blocks=[3, 4, 6, 3],
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m_channels=64,
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feat_dim=80,
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embedding_size=192,
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baseWidth=26,
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scale=2,
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expansion=2,
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pooling_func="TSTP",
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two_emb_layer=False,
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):
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super(ERes2NetV2, self).__init__()
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self.in_planes = m_channels
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self.feat_dim = feat_dim
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self.embedding_size = embedding_size
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self.stats_dim = int(feat_dim / 8) * m_channels * 8
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self.two_emb_layer = two_emb_layer
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self.baseWidth = baseWidth
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self.scale = scale
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self.expansion = expansion
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self.conv1 = nn.Conv2d(1, m_channels, kernel_size=3, stride=1, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(m_channels)
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self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=1)
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self.layer2 = self._make_layer(block, m_channels * 2, num_blocks[1], stride=2)
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self.layer3 = self._make_layer(block_fuse, m_channels * 4, num_blocks[2], stride=2)
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self.layer4 = self._make_layer(block_fuse, m_channels * 8, num_blocks[3], stride=2)
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self.layer3_ds = nn.Conv2d(
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m_channels * 4 * self.expansion, m_channels * 8 * self.expansion,
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kernel_size=3, padding=1, stride=2, bias=False,
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)
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self.fuse34 = AFF(channels=m_channels * 8 * self.expansion, r=4)
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self.n_stats = 1 if pooling_func == "TAP" or pooling_func == "TSDP" else 2
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self.pool = getattr(pooling_layers, pooling_func)(in_dim=self.stats_dim * self.expansion)
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self.seg_1 = nn.Linear(self.stats_dim * self.expansion * self.n_stats, embedding_size)
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if self.two_emb_layer:
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self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False)
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self.seg_2 = nn.Linear(embedding_size, embedding_size)
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else:
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self.seg_bn_1 = nn.Identity()
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self.seg_2 = nn.Identity()
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def _make_layer(self, block, planes, num_blocks, stride):
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strides = [stride] + [1] * (num_blocks - 1)
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layers = []
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for stride in strides:
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layers.append(
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block(self.in_planes, planes, stride, baseWidth=self.baseWidth, scale=self.scale, expansion=self.expansion)
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)
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self.in_planes = planes * self.expansion
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return nn.Sequential(*layers)
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def forward(self, x):
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x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T)
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x = x.unsqueeze_(1)
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out = F.relu(self.bn1(self.conv1(x)))
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out1 = self.layer1(out)
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out2 = self.layer2(out1)
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out3 = self.layer3(out2)
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out4 = self.layer4(out3)
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out3_ds = self.layer3_ds(out3)
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fuse_out34 = self.fuse34(out4, out3_ds)
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stats = self.pool(fuse_out34)
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embed_a = self.seg_1(stats)
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if self.two_emb_layer:
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out = F.relu(embed_a)
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out = self.seg_bn_1(out)
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embed_b = self.seg_2(out)
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return embed_b
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else:
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return embed_a
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@@ -0,0 +1,92 @@
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#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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# Modified from 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker)
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import os
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import time
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import logging
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import torch
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import numpy as np
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from funasr.register import tables
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from funasr.models.campplus.utils import extract_feature
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from funasr.utils.load_utils import load_audio_text_image_video
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from funasr.models.eres2net.eres2netv2 import ERes2NetV2
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@tables.register("model_classes", "ERes2NetV2")
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@tables.register("model_classes", "iic/speech_eres2netv2_sv_zh-cn_16k-common")
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class ERes2NetV2SV(torch.nn.Module):
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def __init__(
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self,
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feat_dim=80,
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embedding_size=192,
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m_channels=64,
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baseWidth=26,
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scale=2,
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expansion=2,
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num_blocks=[3, 4, 6, 3],
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pooling_func="TSTP",
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two_emb_layer=False,
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**kwargs,
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):
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super().__init__()
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self.model = ERes2NetV2(
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feat_dim=feat_dim,
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embedding_size=embedding_size,
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m_channels=m_channels,
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baseWidth=baseWidth,
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scale=scale,
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expansion=expansion,
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num_blocks=num_blocks,
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pooling_func=pooling_func,
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two_emb_layer=two_emb_layer,
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)
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self.embedding_size = embedding_size
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model_path = kwargs.get("model_path", None)
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init_param = kwargs.get("init_param", None)
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if init_param is None and model_path is not None:
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ckpt = os.path.join(model_path, "pretrained_eres2netv2.ckpt")
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if os.path.exists(ckpt):
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init_param = ckpt
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if init_param is not None and os.path.exists(init_param):
|
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self._load_pretrained(init_param)
|
||||
|
||||
def _load_pretrained(self, path):
|
||||
state_dict = torch.load(path, map_location="cpu")
|
||||
if "state_dict" in state_dict:
|
||||
state_dict = state_dict["state_dict"]
|
||||
missing, unexpected = self.model.load_state_dict(state_dict, strict=False)
|
||||
if missing:
|
||||
logging.warning(f"ERes2NetV2 missing keys: {missing[:5]}...")
|
||||
logging.info(f"ERes2NetV2 loaded pretrained weights from {path}")
|
||||
|
||||
def forward(self, x):
|
||||
return self.model(x)
|
||||
|
||||
def inference(
|
||||
self,
|
||||
data_in,
|
||||
data_lengths=None,
|
||||
key: list = None,
|
||||
tokenizer=None,
|
||||
frontend=None,
|
||||
**kwargs,
|
||||
):
|
||||
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, speech_times = extract_feature(audio_sample_list)
|
||||
speech = speech.to(device=kwargs["device"])
|
||||
time3 = time.perf_counter()
|
||||
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
|
||||
meta_data["batch_data_time"] = np.array(speech_times).sum().item() / 16000.0
|
||||
results = [{"spk_embedding": self.forward(speech.to(torch.float32))}]
|
||||
return results, meta_data
|
||||
@@ -18,6 +18,7 @@ tests = [
|
||||
"test_paraformer_streaming.py",
|
||||
"test_qwen3_asr.py",
|
||||
"test_glm_asr.py",
|
||||
"test_eres2netv2.py",
|
||||
]
|
||||
|
||||
SEP = "-" * 60
|
||||
|
||||
@@ -0,0 +1,67 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Test ERes2NetV2: speaker verification/diarization with ASR"""
|
||||
import sys
|
||||
import time
|
||||
|
||||
|
||||
def main():
|
||||
from funasr import AutoModel
|
||||
|
||||
url_zh = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav"
|
||||
|
||||
# Test 1: ERes2NetV2 as spk_model combined with VAD + ASR
|
||||
print("[ERes2NetV2] Loading model with VAD + ASR + SPK...")
|
||||
t0 = time.time()
|
||||
model = AutoModel(
|
||||
model="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
|
||||
vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
|
||||
vad_kwargs={"max_single_segment_time": 60000},
|
||||
spk_model="iic/speech_eres2netv2_sv_zh-cn_16k-common",
|
||||
device="cuda:0",
|
||||
disable_update=True,
|
||||
)
|
||||
print("[ERes2NetV2] Model loaded in %.1fs" % (time.time() - t0))
|
||||
|
||||
# Test with speaker diarization
|
||||
print("[ERes2NetV2] Running inference with speaker diarization...")
|
||||
t0 = time.time()
|
||||
res = model.generate(
|
||||
input=url_zh,
|
||||
batch_size_s=300,
|
||||
)
|
||||
print("[ERes2NetV2] Inference done in %.1fs" % (time.time() - t0))
|
||||
|
||||
if res and len(res) > 0 and "text" in res[0]:
|
||||
print("[ERes2NetV2] Result: %s" % res[0]["text"])
|
||||
if "spk" in res[0]:
|
||||
print("[ERes2NetV2] Speaker: %s" % res[0]["spk"])
|
||||
print("[ERes2NetV2] Test 1 PASSED")
|
||||
else:
|
||||
print("[ERes2NetV2] Test 1 FAILED - no text in result")
|
||||
return 1
|
||||
|
||||
# Test 2: ERes2NetV2 standalone speaker embedding
|
||||
print("[ERes2NetV2] Test 2: Standalone speaker embedding...")
|
||||
t0 = time.time()
|
||||
spk_model = AutoModel(
|
||||
model="iic/speech_eres2netv2_sv_zh-cn_16k-common",
|
||||
device="cuda:0",
|
||||
disable_update=True,
|
||||
)
|
||||
res = spk_model.generate(input=url_zh)
|
||||
print("[ERes2NetV2] Embedding done in %.1fs" % (time.time() - t0))
|
||||
|
||||
if res and len(res) > 0 and "spk_embedding" in res[0]:
|
||||
emb = res[0]["spk_embedding"]
|
||||
print("[ERes2NetV2] Embedding shape: %s" % str(emb.shape))
|
||||
print("[ERes2NetV2] Test 2 PASSED")
|
||||
else:
|
||||
print("[ERes2NetV2] Test 2 FAILED - no spk_embedding in result")
|
||||
return 1
|
||||
|
||||
print("[ERes2NetV2] All tests PASSED")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Reference in New Issue
Block a user