Textual Echo Cancellation (TEC)

Introduction

This repository provides a standalone, open-source Python reproduction of Textual Echo Cancellation (TEC) based on the IEEE SLT 2021 paper:

Textual Echo Cancellation Shaojin Ding, Ye Jia, Ke Hu, Quan Wang Paper: https://arxiv.org/pdf/2008.06006 | Audio Demo Page: https://google.github.io/speaker-id/publications/TEC/

When a user speaks to a smart speaker or voice-enabled device while the device is playing back a Text-to-Speech (TTS) response, the microphone captures a reverberant mixture of the user's speech and the device's TTS playback. Classical Acoustic Echo Cancellation (AEC) requires streaming the full high-bandwidth TTS reference waveform to the echo canceller. Textual Echo Cancellation (TEC) instead uses the lightweight text transcript of the interfering TTS prompt (< 1 KB) as a side input to a multi-source attention sequence-to-sequence neural network, canceling the interfering TTS echo and reconstructing the clean user speech spectrogram and waveform.

   

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Features

• Complete Model Suite:
  • TecModel: Multi-source sequence-to-sequence model taking noisy/reverberant speech (SpeechEncoderV1) and interfering TTS text (TtsEncoderV2) with MultiSourceFbeDecoderV1 (GmmMonotonicAttention or AdditiveAttention).
  • AecModel (AEC-Seq2seq): Neural baseline taking noisy/reverberant speech and clean reference TTS audio with dual speech encoders and multi-source attention.
  • VanillaSeq2SeqModel (NoSideInput): Single-source sequence-to-sequence speech enhancement baseline without side input.
  • NlmsAec (AEC-NLMS): Classical Normalized Least Mean Squares adaptive filter baseline.
• Standalone Lingvo Implementation: Built on open-source lingvo and tensorflow, with zero dependencies on proprietary internal libraries.

- **End-to-End Pipelines & CLI Scripts**: - **Dataset Preparation** (`scripts/prepare_data.py`): Pairs clean speech (LibriTTS) with longer interfering TTS utterances (LJSpeech / VCTK), simulates room impulse response (RIR) reverberation, mixes at a target SNR (default 0 dB), pads trailing zeros, and writes `TFRecord` datasets. - **Model Training** (`scripts/train.py`): Trains any registered Lingvo configuration (`TecSingleInterfering`, `TecMultiInterfering`, `AecSingleInterfering`, `NoSideInputSingleInterfering`, etc.). - **Inference & Waveform Synthesis** (`scripts/inference.py`): Predicts enhanced log-Mel spectrograms and synthesizes 24 kHz waveforms via Griffin-Lim phase reconstruction (`WaveformProcessor`). - **Evaluation** (`scripts/evaluate.py`): Computes 13-MFCC Mel Cepstral Distortion (MCD) with Dynamic Time Warping (DTW), punctuation-normalized Word Error Rate (WER), and model FLOPS / side-input bandwidth. - **TFLite Export** (`scripts/export_tflite.py`): Exports trained models to `.tflite` FlatBuffer format (with optional dynamic range quantization) and validates on-device execution with `tf.lite.Interpreter`.

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Installation

Install from PyPI:

pip3 install textual-echo-cancellation

Or install from source:

git clone https://github.com/wq2012/tec.git
cd tec
pip3 install -r requirements.txt
pip3 install -e .

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Quickstart

1. Prepare Training & Evaluation Datasets

You can prepare a TFRecord dataset from CSV manifests (utt_id,wav_path,transcript) of clean speech (e.g., LibriTTS) and interfering TTS speech (e.g., LJSpeech or VCTK), or generate a synthetic dataset for testing:

# Generate a synthetic TFRecord dataset for quick testing:
python3 scripts/prepare_data.py \
  --generate_synthetic \
  --num_synthetic 16 \
  --snr_db 0.0 \
  --reverb_rt60 0.25 \
  --output_tfrecord /tmp/tec_data/train.tfrecord

# Or prepare from LibriTTS + LJSpeech CSV manifests:
python3 scripts/prepare_data.py \
  --clean_manifest_csv /path/to/libritts_train.csv \
  --interfering_manifest_csv /path/to/ljspeech_train.csv \
  --snr_db 0.0 \
  --reverb_rt60 0.25 \
  --output_tfrecord /tmp/tec_data/train.tfrecord

2. Train the Model

python3 scripts/train.py \
  --model TecSingleInterfering \
  --train_file_pattern "/tmp/tec_data/train.tfrecord" \
  --logdir /tmp/tec_checkpoints \
  --max_steps 100 \
  --batch_size 4 \
  --learning_rate 1e-3

Available --model configurations:

• TecSingleInterfering: TEC model (speech + TTS text) for single-speaker TTS interference (LibriTTS + LJSpeech).
• TecMultiInterfering: TEC model (speech + TTS text) for multi-speaker TTS interference (LibriTTS + VCTK).
• AecSingleInterfering / AecMultiInterfering: AEC-Seq2seq baseline (speech + TTS reference audio).
• NoSideInputSingleInterfering / NoSideInputMultiInterfering: Vanilla-Seq2seq baseline (speech mixture only).

3. Run Inference

python3 scripts/inference.py \
  --model TecSingleInterfering \
  --checkpoint_path /tmp/tec_checkpoints/model.ckpt-100 \
  --mixed_wav /path/to/mixed_input.wav \
  --interfering_text "currently in mountain view it is 72 degrees" \
  --output_wav /tmp/enhanced_clean.wav

4. Evaluate MCD, WER, and Model Complexity

python3 scripts/evaluate.py \
  --ref_wav /path/to/clean_reference.wav \
  --pred_wav /tmp/enhanced_clean.wav \
  --ref_transcript "Turn off the bedroom lights!" \
  --hyp_transcript "turn off the bedroom lights" \
  --print_complexity

5. Export to TensorFlow Lite (.tflite)

python3 scripts/export_tflite.py \
  --model TecSingleInterfering \
  --output_tflite /tmp/tec_model.tflite \
  --num_frames 32 \
  --text_length 16 \
  --decode_steps 8 \
  --quantize \
  --verify

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Published Paper Reference Results

For reference, Table 3 of the original paper (arXiv:2008.06006v4) reported the following results using Google's internal speech infrastructure on 24 kHz LibriTTS mixed at 0 dB SNR with reverberant LJ Speech (single interfering voice) and VCTK (multiple interfering voices):

Condition	Method	WER (%) test-clean ↓	WER (%) test-other ↓	MCD (dB) test-clean ↓	MCD (dB) test-other ↓	MOS test-clean ↑	MOS test-other ↑	Side input (KB) ↓	GFLOPS ↓
Ground-truth LibriTTS	-	2.30	4.50	0.00	0.00	4.43 ± 0.04	3.82 ± 0.06	-	-
Single interfering voice (LibriTTS + LJ Speech)	Microphone signal	89.9	120.5	18.83	21.44	-	-	-	-
	AEC-NLMS	48.6	60.1	12.26	12.57	1.95 ± 0.10	1.28 ± 0.09	310	0
	Vanilla-Seq2seq	25.4	54.0	7.85	8.84	1.99 ± 0.06	1.47 ± 0.05	0	6.32
	AEC-Seq2seq	8.30	24.3	6.38	7.07	2.77 ± 0.07	1.90 ± 0.06	310	9.51
	TEC (proposed)	15.5	39.8	7.51	8.54	2.20 ± 0.07	1.65 ± 0.06	0.10	7.27
Multiple interfering voices (LibriTTS + VCTK)	Microphone signal	29.7	44.6	10.75	12.88	-	-	-	-
	AEC-NLMS	15.5	35.5	6.57	8.13	2.06 ± 0.11	1.60 ± 0.08	230	0
	Vanilla-Seq2seq	19.7	38.7	7.53	8.87	2.16 ± 0.07	1.50 ± 0.05	0	6.32
	AEC-Seq2seq	6.90	19.8	5.04	5.72	2.90 ± 0.07	2.03 ± 0.07	230	8.62
	TEC (proposed)	14.8	32.5	6.46	7.71	2.39 ± 0.07	1.70 ± 0.06	0.06	6.90

⋆ Note (Table 3 & Section 3.4–3.5 of the paper):

• The side input size and GFLOPS in the two conditions are different since the average lengths of the echo signal are different in the two conditions (~7 seconds per utterance in LJ Speech vs. ~2 seconds per utterance in VCTK).
• In the paper, models were trained on 2×2 TPU slices with a global batch size of 32 using the Adam optimizer ($\beta_1=0.9$, $\beta_2=0.999$, $\epsilon=10^{-6}$) and an initial learning rate of $10^{-4}$ exponentially decaying to $10^{-5}$ after 50,000 iterations.

Open-Source Reproduction Results

Using the standalone data preparation (scripts/prepare_data.py), training (scripts/train.py), and evaluation (scripts/evaluate.py) pipelines in this repository on the open-source LibriTTS (train-clean-100, test-clean, test-other), LJSpeech-1.1 (90%/10% split), and VCTK-0.92 (90%/10% per-speaker split across 109 speakers) datasets at 24 kHz (mixed at 0 dB SNR with synthetic room impulse responses at RT60 = 0.25 s, trained for 60 steps on CPU with batch_size=4, learning_rate=1e-3, and evaluated with local Qwen3-ASR-0.6B-F16 via audio.cpp on 20 utterances per test split):

Condition	Method	WER (%) test-clean ↓	WER (%) test-other ↓	MCD (dB) test-clean ↓	MCD (dB) test-other ↓	Side Input test-clean (KB) ↓	Side Input test-other (KB) ↓
Ground-truth LibriTTS	GroundTruth	3.52 (7/199)	6.78 (12/177)	0.00	0.00	0.000	0.000
Single interfering voice (LibriTTS + LJSpeech)	MicrophoneSignal	90.45 (180/199)	114.12 (202/177)	12.86	14.61	0.000	0.000
	NlmsAec (AEC-NLMS)	88.44 (176/199)	107.34 (190/177)	12.80	14.48	243.465	209.085
	NoSideInputSingleInterfering (Vanilla-Seq2seq)	45.23 (90/199)	91.53 (162/177)	9.58	11.34	0.000	0.000
	AecSingleInterfering (AEC-Seq2seq)	12.06 (24/199)	23.16 (41/177)	8.85	9.86	243.465	209.085
	TecSingleInterfering (TEC)	21.61 (43/199)	46.89 (83/177)	8.24	9.28	0.076	0.068
Ground-truth LibriTTS (Multi split)	GroundTruth	5.03 (10/199)	7.82 (19/243)	0.00	0.00	0.000	0.000
Multiple interfering voices (LibriTTS + VCTK)	MicrophoneSignal	34.17 (68/199)	48.97 (119/243)	7.67	7.70	0.000	0.000
	NlmsAec (AEC-NLMS)	28.64 (57/199)	34.98 (85/243)	7.92	8.40	186.922	206.759
	NoSideInputMultiInterfering (Vanilla-Seq2seq)	31.16 (62/199)	42.39 (103/243)	7.93	8.72	0.000	0.000
	AecMultiInterfering (AEC-Seq2seq)	8.54 (17/199)	22.22 (54/243)	7.80	7.88	186.922	206.759
	TecMultiInterfering (TEC)	26.63 (53/199)	45.27 (110/243)	7.96	8.40	0.037	0.039

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Running Unit Tests

To run the full test suite locally:

bash run_tests.sh

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Citation

If you find this library useful in your research, please cite the 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={653--660},
  year={2021},
  organization={IEEE}
}