TriLinSL/lid-ecapa-voxlingua107-si-ta-en

Spoken language ID: Sinhala / Tamil / English

speechbrain/lang-id-voxlingua107-ecapa (ECAPA-TDNN) fine-tuned into a 3-class classifier (si: Sinhala, ta: Tamil, en: English) to route audio to language-specific ASR models. The output layer was initialised from the pretrained rows of these 3 languages, so the zero-shot numbers below are the original model restricted to the same 3 classes.

Test results

Each clip is classified from its first N seconds (what a streaming router sees); full = up to 10 s. Test sets: up to 1500 clips per (language, source), speaker-diverse, from the test_* splits.

	zero-shot acc	fine-tuned acc	zero-shot macro-F1	fine-tuned macro-F1
1s	0.6625	0.8810	0.5807	0.8829
2s	0.9078	0.9745	0.8920	0.9731
3s	0.9588	0.9892	0.9532	0.9883
full	0.9838	0.9943	0.9833	0.9938

Accuracy per (language, source):

	1s	2s	3s	full
acc_en__libritts_r	0.9753	0.9993	1.0000	1.0000
acc_si__openslr_sinhala	0.9600	0.9793	0.9887	0.9940
acc_ta__common_voice_tamil	0.6400	0.9433	0.9813	0.9873
acc_ta__indic_tts_tamil	0.9487	0.9760	0.9867	0.9960

Best epoch: 5 (selected on mean val macro-F1 over 1s, 2s, 3s, full).

Usage

from speechbrain.inference.classifiers import EncoderClassifier
lid = EncoderClassifier.from_hparams(source="TriLinSL/lid-ecapa-voxlingua107-si-ta-en", savedir="pretrained_lid")
out_prob, score, index, label = lid.classify_file("audio.wav")   # 16 kHz mono
print(label[0], out_prob.exp()[0])                                  # out_prob are log-probabilities

For routing, classify the first 2–3 s; if the top probability is below ~0.8, wait for more audio and classify again.

Training

• Data: TriLingDATA/si-ta-en-asr, 20 h per language, balanced across sources, round-robin by speaker, val/test speakers removed from training. Selected clip ids are in data_selection/.

	clips	hours	speakers
en	indic_tts_tamil	77.0000	0.2200
en	libritts_r	10984.0000	20.0000
en	openslr_sinhala	1352.0000	1.4400
si	openslr_sinhala	16509.0000	20.0000
ta	common_voice_tamil	5617.0000	10.0000
ta	indic_tts_tamil	1592.0000	10.0000

• Random 1–4 s crops; speed, synthetic reverb, telephone band, noise and gain augmentation; equal sampling per language every step.
• 12 epochs × 250 steps, batch 64, head-only for 1 epoch(s), AdamW (backbone 0.0001, head 0.001), warm-up 200 + cosine, label smoothing 0.05, seed 3407.

Limitations

• Each language comes from few recording setups (Sinhala: read sentences; Tamil: IndicTTS talk-style speech and Common Voice; English: LibriTTS-R audiobooks). The model may partly rely on recording conditions. Check it on audio from your real users before relying on it.
• English training data is native-accented audiobook speech, not Sri Lankan English.
• Code-mixed speech is labelled by its main language; utterance-level output only.