wq2012/aec_single_interfering

AEC-Seq2seq Neural Baseline — Single Interfering Voice (AecSingleInterfering)

Multi-source attention sequence-to-sequence Acoustic Echo Cancellation (AEC-Seq2seq) neural baseline model trained on 24 kHz LibriTTS user speech mixed at 0 dB SNR with reverberant LJ Speech (single-speaker TTS interference). Uses dual audio encoders (SpeechEncoderV1) on both the microphone mixture and the full reference TTS playback audio (~240–310 KB side input).

• GitHub Repository: https://github.com/wq2012/tec
• PyPI Package: textual-echo-cancellation
• Paper: Textual Echo Cancellation (IEEE SLT 2021, arXiv:2008.06006v4)
• Audio Demo Page: https://google.github.io/speaker-id/publications/TEC/

Open-Source Reproduction Notice: This pretrained model was trained using the standalone open-source reproduction library (wq2012/tec) built on lingvo and tensorflow. It does not use Google's proprietary internal codebase or internal training data infrastructure.

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Model Performance

1. Open-Source Reproduction Evaluation (AecSingleInterfering)

Evaluated on 24 kHz LibriTTS (test-clean, test-other) mixed at 0 dB SNR with synthetic room impulse responses (RT60 = 0.25 s) under the Single interfering voice (LibriTTS + LJ Speech) condition, using Qwen3-ASR-0.6B-F16 via audio.cpp for ASR WER scoring and 13-MFCC Dynamic Time Warping for MCD:

Metric	test-clean	test-other	Paper Reference (AEC-Seq2seq)
WER (%) ↓	12.06% (24/199)	23.16% (41/177)	8.30% / 24.3%
MCD (dB) ↓	8.85 dB	9.86 dB	6.38 dB / 7.07 dB
Side Input Size (KB) ↓	243.465 KB	209.085 KB	310 KB
Computational Complexity ↓	9.51 GFLOPS	9.51 GFLOPS	9.51 GFLOPS

2. Full Comparison Across All Pretrained Models & Baselines

Condition	Method	Hugging Face Model	WER (%) test-clean ↓	WER (%) test-other ↓	MCD (dB) test-clean ↓	MCD (dB) test-other ↓	Side Input test-clean (KB) ↓
Single interfering voice (LibriTTS + LJSpeech)	GroundTruth	—	3.52	6.78	0.00	0.00	0.000
	MicrophoneSignal	—	90.45	114.12	12.86	14.61	0.000
	NlmsAec (AEC-NLMS)	—	88.44	107.34	12.80	14.48	243.465
	NoSideInputSingleInterfering	wq2012/vanilla_seq2seq_single_interfering	45.23	91.53	9.58	11.34	0.000
	AecSingleInterfering	wq2012/aec_single_interfering	12.06	23.16	8.85	9.86	243.465
	TecSingleInterfering	wq2012/tec_single_interfering	21.61	46.89	8.24	9.28	0.076
Multiple interfering voices (LibriTTS + VCTK)	GroundTruth	—	5.03	7.82	0.00	0.00	0.000
	MicrophoneSignal	—	34.17	48.97	7.67	7.70	0.000
	NlmsAec (AEC-NLMS)	—	28.64	34.98	7.92	8.40	186.922
	NoSideInputMultiInterfering	wq2012/vanilla_seq2seq_multi_interfering	31.16	42.39	7.93	8.72	0.000
	AecMultiInterfering	wq2012/aec_multi_interfering	8.54	22.22	7.80	7.88	186.922
	TecMultiInterfering	wq2012/tec_multi_interfering	26.63	45.27	7.96	8.40	0.037

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Files in This Repository

• best.ckpt.data-00000-of-00001, best.ckpt.index, best.ckpt.meta, checkpoint: TensorFlow / Lingvo checkpoint for AecSingleInterfering.
• model.tflite: Dynamic-range quantized TensorFlow Lite (.tflite) FlatBuffer model for on-device inference.
• evaluation_metrics.json: Verified evaluation results (test-clean and test-other) for AecSingleInterfering.

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How to Use

1. Install textual-echo-cancellation

pip3 install textual-echo-cancellation huggingface_hub

2. Download the Model from Hugging Face

from huggingface_hub import snapshot_download

model_dir = snapshot_download(repo_id="wq2012/aec_single_interfering")
print("Downloaded model to:", model_dir)

3. Run Inference via CLI (scripts/inference.py)

python3 -m scripts.inference \
  --model AecSingleInterfering \
  --checkpoint_path "${MODEL_DIR}/best.ckpt" \
  --mixed_wav /path/to/mixed_input.wav \
  --interfering_wav /path/to/reference_tts.wav \
  --output_wav /tmp/enhanced_clean.wav

4. Run Inference via Python API

import os
from huggingface_hub import snapshot_download
from tec import inference

model_dir = snapshot_download(repo_id="wq2012/aec_single_interfering")
ckpt_path = os.path.join(model_dir, "best.ckpt")

result = inference.run_inference_on_wav(
    model_name="AecSingleInterfering",
    mixed_wav_path="/path/to/mixed_input.wav",
    interfering_text="currently in mountain view it is 72 degrees",
    checkpoint_path=ckpt_path,
    output_wav_path="/tmp/enhanced_clean.wav",
)
print("Enhanced log-Mel spectrogram shape:", result["predicted_mel"].shape)

5. On-Device Inference with Quantized TFLite (model.tflite)

import os
import numpy as np
import tensorflow as tf
from huggingface_hub import snapshot_download

model_dir = snapshot_download(repo_id="wq2012/aec_single_interfering")
tflite_path = os.path.join(model_dir, "model.tflite")

interpreter = tf.lite.Interpreter(model_path=tflite_path)
interpreter.allocate_tensors()

for detail in interpreter.get_input_details():
    interpreter.set_tensor(
        detail["index"], np.zeros(detail["shape"], dtype=detail["dtype"])
    )

interpreter.invoke()
output_details = interpreter.get_output_details()
enhanced_mel = interpreter.get_tensor(output_details[0]["index"])
print("TFLite predicted log-Mel shape:", enhanced_mel.shape)

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Citation

If you use this model or the textual-echo-cancellation library in your research, please cite the original paper:

@inproceedings{ding2021textual,
  title={Textual Echo Cancellation},
  author={Ding, Shaojin and Jia, Ye and Hu, Ke and Wang, Quan},
  booktitle={2021 IEEE Spoken Language Technology Workshop (SLT)},
  pages={666--673},
  year={2021},
  organization={IEEE}
}