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说明文档

Parakeet-TDT 1.1B ONNX

NVIDIA 的 Parakeet-TDT 1.1B 预导出 ONNX 模型，可直接使用，无需 Docker/NeMo 导出。

模型描述

  • 基础模型：nvidia/parakeet-tdt-1.1b
  • 架构：Token-and-Duration Transducer (TDT)，采用 FastConformer 编码器
  • 参数量：11 亿
  • 特征：80 维梅尔滤波器组特征（16kHz）
  • 词表：1024 个 SentencePiece 词元 + 空白词元
  • 格式：ONNX Runtime 兼容（CPU/GPU）

包含内容

量化模型（推荐 - 总计 3.8GB）

  • encoder.int8.onnx（1.1GB）- INT8 量化编码器，外部权重
  • encoder.int8.weights（2.0GB）- INT8 编码器权重文件
  • decoder.int8.onnx（7.0MB）- INT8 量化解码器
  • joiner.int8.onnx（1.7MB）- INT8 量化联合器
  • tokens.txt（11KB）- 词表文件

全精度模型（可选 - 额外 8.1GB）

  • encoder.onnx（41MB）+ encoder.weights（4.0GB）- FP32 编码器
  • decoder.onnx（28MB）- FP32 解码器
  • joiner.onnx（6.6MB）- FP32 联合器
  • 639 个层权重文件（大小各异）

快速开始

下载模型

# 下载所有模型（12GB）
huggingface-cli download jenerallee78/parakeet-tdt-1.1b-onnx --local-dir ./models

# 或仅下载 INT8 量化版（3.8GB - 推荐）
huggingface-cli download jenerallee78/parakeet-tdt-1.1b-onnx \
  --include "encoder.int8.*" "decoder.int8.onnx" "joiner.int8.onnx" "tokens.txt" \
  --local-dir ./models

使用 ONNX Runtime（Python）

import onnxruntime as ort
import numpy as np

# 加载模型
encoder_session = ort.InferenceSession("models/encoder.int8.onnx")
decoder_session = ort.InferenceSession("models/decoder.int8.onnx")  
joiner_session = ort.InferenceSession("models/joiner.int8.onnx")

# 加载词表
with open("models/tokens.txt") as f:
    vocab = [line.split()[0] for line in f]

# 推理（简化示例）
# ... （添加梅尔特征提取）
encoder_out = encoder_session.run(None, {"audio_signal": mel_features})[0]
# ... （添加使用解码器和联合器的解码循环）

使用 Rust

完整 Rust 示例请参见 Swictation STT 实现。

验证

这些模型已验证与原始 NeMo 导出产生完全相同的输出：

  • ✅ 测试音频转录正确
  • ✅ MD5 校验和与原始导出匹配
  • ✅ 元数据已验证（feat_dim: 80，vocab_size: 1024）
  • ✅ 已使用短音频（6秒）和长音频（84秒）测试

测试结果

  • 短音频：100% 准确率 - "hello world testing one two three"
  • 长音频：95% 准确率 - 复杂技术文章（8410 帧中 375 个词元）

导出详情

导出脚本：可在 Swictation 仓库 中找到

导出方法：

docker run --rm -v $(pwd):/workspace -w /workspace \
  nvcr.io/nvidia/nemo:25.07 \
  bash -c "pip install onnxruntime && python3 export_parakeet_tdt_1.1b.py"

性能

  • WER：1.39%（LibriSpeech test-clean，来自基础模型）
  • 速度：比 RNNT 基线快 64%
  • 推理：现代 CPU 上实时，GPU 上更快
  • 空白率：28.6%（非常适合连续语音）

技术细节

模型架构

  • 编码器：42 层 FastConformer（1024 维）
  • 解码器：2 层 LSTM（640 维隐藏层）
  • 联合器：前馈网络
  • 下采样：8x（8410 梅尔帧 → 1051 编码器帧）

元数据

{
  "vocab_size": 1024,
  "normalize_type": "per_feature",
  "pred_rnn_layers": 2,
  "pred_hidden": 640,
  "subsampling_factor": 8,
  "model_type": "EncDecRNNTBPEModel",
  "feat_dim": 80
}

许可证

与原始 NVIDIA Parakeet-TDT 1.1B 模型相同（CC-BY-4.0）。

引用

@misc{parakeet-tdt-1.1b-onnx,
  author = {Robert Lee},
  title = {Parakeet-TDT 1.1B ONNX Export},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/jenerallee78/parakeet-tdt-1.1b-onnx}},
  note = {Verified ONNX export of NVIDIA's Parakeet-TDT 1.1B model}
}

原始模型：

@misc{nvidia-parakeet-tdt,
  author = {NVIDIA},
  title = {Parakeet-TDT 1.1B},
  year = {2024},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/nvidia/parakeet-tdt-1.1b}}
}

致谢

  • 基础模型：NVIDIA Research
  • ONNX 导出：Swictation 项目
  • 验证：使用 Rust ONNX Runtime 实现进行了广泛测试

jenerallee78/parakeet-tdt-1.1b-onnx

作者 jenerallee78

automatic-speech-recognition onnxruntime
↓ 0 ♥ 0

创建时间: 2025-11-10 23:04:36+00:00

更新时间: 2025-11-10 23:22:45+00:00

在 Hugging Face 上查看

文件 (651)

AHA_EXPORT_BUG_ANALYSIS.md
AHA_PHYSICAL_ACOUSTIC_VALIDATION.md
Constant_2929_attr__value
HF_README.md
README.md
TESTING_NOTES.md
decoder.int8.onnx ONNX
decoder.onnx ONNX
encoder.int8.onnx ONNX
encoder.int8.weights
encoder.onnx ONNX
encoder.weights
joiner.int8.onnx ONNX
joiner.onnx ONNX
layers.0.conv.pointwise_conv1.bias
layers.0.conv.pointwise_conv1.weight
layers.0.conv.pointwise_conv2.weight
layers.0.feed_forward1.linear1.bias
layers.0.feed_forward2.linear1.bias
layers.1.conv.pointwise_conv1.bias
layers.1.conv.pointwise_conv1.weight
layers.1.conv.pointwise_conv2.weight
layers.1.feed_forward1.linear1.bias
layers.1.feed_forward2.linear1.bias
layers.10.conv.pointwise_conv1.bias
layers.10.conv.pointwise_conv1.weight
layers.10.conv.pointwise_conv2.weight
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onnx__MatMul_12879
onnx__MatMul_12880
onnx__MatMul_12890
onnx__MatMul_12891
onnx__MatMul_12892
onnx__MatMul_12908
onnx__MatMul_12914
onnx__MatMul_12915
onnx__MatMul_12916
onnx__MatMul_12917
onnx__MatMul_12918
onnx__MatMul_12928
onnx__MatMul_12929
onnx__MatMul_12930
onnx__MatMul_12946
onnx__MatMul_12952
onnx__MatMul_12953
onnx__MatMul_12954
onnx__MatMul_12955
onnx__MatMul_12956
onnx__MatMul_12966
onnx__MatMul_12967
onnx__MatMul_12968
onnx__MatMul_12984
onnx__MatMul_12990
onnx__MatMul_12991
onnx__MatMul_12992
onnx__MatMul_12993
onnx__MatMul_12994
onnx__MatMul_13004
onnx__MatMul_13005
onnx__MatMul_13006
onnx__MatMul_13022
onnx__MatMul_13028
onnx__MatMul_13029
onnx__MatMul_13030
onnx__MatMul_13031
onnx__MatMul_13032
onnx__MatMul_13042
onnx__MatMul_13043
onnx__MatMul_13044
onnx__MatMul_13060
onnx__MatMul_13066
onnx__MatMul_13067
onnx__MatMul_13068
onnx__MatMul_13069
onnx__MatMul_13070
onnx__MatMul_13080
onnx__MatMul_13081
onnx__MatMul_13082
onnx__MatMul_13098
onnx__MatMul_13104
onnx__MatMul_13105
onnx__MatMul_13106
onnx__MatMul_13107
onnx__MatMul_13108
onnx__MatMul_13118
onnx__MatMul_13119
onnx__MatMul_13120
onnx__MatMul_13136
onnx__MatMul_13142
onnx__MatMul_13143
onnx__MatMul_13144
onnx__MatMul_13145
onnx__MatMul_13146
onnx__MatMul_13156
onnx__MatMul_13157
onnx__MatMul_13158
onnx__MatMul_13174
onnx__MatMul_13180
onnx__MatMul_13181
onnx__MatMul_13182
onnx__MatMul_13183
onnx__MatMul_13184
onnx__MatMul_13194
onnx__MatMul_13195
onnx__MatMul_13196
onnx__MatMul_13212
onnx__MatMul_13218
onnx__MatMul_13219
onnx__MatMul_13220
onnx__MatMul_13221
onnx__MatMul_13222
onnx__MatMul_13232
onnx__MatMul_13233
onnx__MatMul_13234
onnx__MatMul_13250
onnx__MatMul_13256
onnx__MatMul_13257
onnx__MatMul_13258
onnx__MatMul_13259
onnx__MatMul_13260
onnx__MatMul_13270
onnx__MatMul_13271
onnx__MatMul_13272
onnx__MatMul_13288
onnx__MatMul_13294
onnx__MatMul_13295
onnx__MatMul_13296
onnx__MatMul_13297
onnx__MatMul_13298
onnx__MatMul_13308
onnx__MatMul_13309
onnx__MatMul_13310
onnx__MatMul_13326
onnx__MatMul_13332
onnx__MatMul_13333
onnx__MatMul_13334
onnx__MatMul_13335
onnx__MatMul_13336
onnx__MatMul_13346
onnx__MatMul_13347
onnx__MatMul_13348
onnx__MatMul_13364
onnx__MatMul_13370
onnx__MatMul_13371
onnx__MatMul_13372
onnx__MatMul_13373
onnx__MatMul_13374
onnx__MatMul_13384
onnx__MatMul_13385
onnx__MatMul_13386
onnx__MatMul_13402
onnx__MatMul_13408
onnx__MatMul_13409
onnx__MatMul_13410
onnx__MatMul_13411
onnx__MatMul_13412
onnx__MatMul_13422
onnx__MatMul_13423
onnx__MatMul_13424
onnx__MatMul_13440
onnx__MatMul_13446
onnx__MatMul_13447
onnx__MatMul_13448
onnx__MatMul_13449
onnx__MatMul_13450
onnx__MatMul_13460
onnx__MatMul_13461
onnx__MatMul_13462
onnx__MatMul_13478
onnx__MatMul_13484
onnx__MatMul_13485
onnx__MatMul_13486
onnx__MatMul_13487
onnx__MatMul_13488
onnx__MatMul_13498
onnx__MatMul_13499
onnx__MatMul_13500
onnx__MatMul_13516
onnx__MatMul_13522
onnx__MatMul_13523
onnx__MatMul_13524
onnx__MatMul_13525
onnx__MatMul_13526
onnx__MatMul_13536
onnx__MatMul_13537
onnx__MatMul_13538
onnx__MatMul_13554
onnx__MatMul_13560
onnx__MatMul_13561
pre_encode.conv.0.weight
pre_encode.conv.2.weight
pre_encode.conv.3.weight
pre_encode.conv.5.weight
pre_encode.conv.6.weight
tokens.txt