turkmedstt/turkish-asr-benchmark

TurkMedSTT Turkish ASR Benchmark

This repository publishes general-domain Turkish ASR benchmark metrics produced by Muhammed Kumcu and Yagmur Tuncer.

Interactive table: turkmedstt/turkish-asr-leaderboard

The release compares 20 ASR models on 1,060 clips (about 105 minutes) drawn from three established Turkish speech sources. It contains metrics only. Source audio, reference transcripts, generated hypotheses, local paths, and medical evaluation results are not distributed.

Evaluation protocol

• All published models are evaluated on the same 1,060 general-domain clips.
• Each model produces one hypothesis for each clip.
• Text normalization and metric computation follow the same benchmark pipeline.
• Overall values are macro averages over clips, so every clip contributes equally.
• The primary ranking key is mean WER. CER and RTF are supporting metrics.
• AcoSemantic values are complementary analysis, not a replacement for accuracy.
• RTF comes from the original Colab A100 runs and depends on backend, model implementation, batching, hardware, and decoding choices.

Dataset composition

Source	Clips	Role in the benchmark
Common Voice Turkish	447	Multi-speaker community recordings and short general utterances
ISSAI Turkish	453	Read-speech recordings with different sentence and speaker characteristics
OpenSLR 106 Turkish	160	Short Turkish utterances from a separately distributed speech corpus
Total	1,060	About 105 minutes of general-domain Turkish speech

The release does not redistribute source audio or transcripts. Dataset names and de-identified clip identifiers are retained only to describe provenance and join metric tables. Original source licenses and access conditions continue to apply.

Column guide

Column	Meaning	Direction and example
Rank	Position after sorting by mean WER	Lower rank number is better in the primary ranking
Model	Hugging Face model identifier	Identifies the evaluated checkpoint
Backend	Inference implementation used in the run	Backends can affect speed and decoding behavior
Clips	Number of evaluated general-domain clips	All published rows contain 1,060 clips
WER	Word error rate: (substitutions + deletions + insertions) / reference words	Lower is better. 0.1345 means about 13.45 word errors per 100 reference words
CER	Character error rate using the same edit operations at character level	Lower is better; useful for Turkish suffix and spelling differences
RTF	Real-time factor: processing seconds / audio seconds	Lower is faster. 0.10 means roughly 10x faster than real time
ASCS	1 - abs(reference sentiment - hypothesis sentiment)	Higher is better; measures preservation of text-level semantic-affective score
ASCS sigma	Population standard deviation of clip-level ASCS values	Lower means more consistent ASCS behavior across clips
ASCS-WER r	Pearson correlation between clip-level WER and ASCS	Negative is expected: more transcription error generally accompanies lower ASCS
Reference sentiment	Mean sentiment score of reference transcripts	Describes the test distribution; it is not a model quality score
Hypothesis sentiment	Mean sentiment score of model outputs	Compare with reference sentiment to inspect systematic affective shift
Sentiment drift	Mean absolute difference between reference and hypothesis sentiment	Lower is better; numerically equal to 1 - mean ASCS in this definition

WER and CER may exceed 1.0 on individual clips when a model inserts many extra tokens. A low RTF does not imply high transcription quality, and a high ASCS does not imply that the words are correct. The metrics must be interpreted together.

Full leaderboard

WER and CER are macro means over clips. RTF is the mean real-time factor measured in the original Colab A100 runs. ASCS_text measures text-level semantic-affective consistency; higher is better. ASCS-WER r is the per-model Pearson correlation across clips. A negative value means higher WER generally coincides with lower semantic-affective consistency.

Rank	Model	Backend	WER ↓	CER ↓	RTF ↓	ASCS ↑	ASCS σ ↓	ASCS-WER r	Sentiment drift ↓
1	openai/whisper-large-v3	transformers	0.1345	0.0588	0.1349	0.9571	0.0563	-0.3471	0.0429
2	vincespeed/faster-whisper-large-v3-turbo-turkish	faster-whisper	0.1825	0.0774	0.0927	0.9534	0.0579	-0.1874	0.0466
3	Huseyin/whisper-large-v3-turkish-finetuned	transformers	0.1841	0.0971	0.1587	0.9510	0.0772	-0.4160	0.0472
4	openai/whisper-large-v2	transformers	0.1901	0.0999	0.1333	0.9498	0.0633	-0.3140	0.0502
5	openai/whisper-large-v3-turbo	transformers	0.2014	0.0897	0.0338	0.9487	0.0687	-0.4941	0.0513
6	facebook/seamless-m4t-v2-large	transformers_pipeline	0.2079	0.0961	0.2848	0.9609	0.0527	-0.3199	0.0391
7	openai/whisper-medium	openai-whisper	0.2143	0.1069	0.2151	0.9476	0.0791	-0.3452	0.0498
8	mpoyraz/wav2vec2-xls-r-300m-cv8-turkish	transformers_ctc	0.2903	0.0665	0.1256	0.9418	0.0550	-0.0986	0.0582
9	openai/whisper-small	openai-whisper	0.3000	0.1327	0.1178	0.9448	0.0638	-0.2163	0.0543
10	facebook/mms-1b-fl102	transformers_ctc	0.4612	0.1454	0.3744	0.9297	0.0915	-0.2346	0.0657
11	ceyda/wav2vec2-large-xlsr-53-turkish	transformers_ctc	0.4832	0.1118	0.1254	0.9320	0.0618	-0.1680	0.0680
12	m3hrdadfi/wav2vec2-large-xlsr-turkish	transformers_ctc	0.4847	0.1102	0.1236	0.9316	0.0603	-0.1159	0.0684
13	cahya/wav2vec2-large-xlsr-turkish	transformers_ctc	0.5218	0.1404	0.1554	0.9323	0.0604	-0.1190	0.0677
14	mbsouksu/wav2vec2-large-xlsr-turkish-large	transformers_ctc	0.5224	0.1266	0.2073	0.9293	0.0648	-0.1799	0.0707
15	openai/whisper-base	openai-whisper	0.5238	0.2291	0.1726	0.9324	0.0663	-0.2435	0.0676
16	selimc/whisper-large-v3-turbo-turkish	transformers	0.5382	0.3481	0.0374	0.9510	0.0604	-0.1126	0.0490
17	Baybars/wav2vec2-xls-r-300m-cv8-turkish	transformers_ctc	0.5550	0.1393	0.2130	0.9297	0.0634	-0.1478	0.0703
18	erenfazlioglu/whisper-small-turkish-tr-best	transformers	0.6195	0.3500	0.0843	0.9515	0.0580	-0.1228	0.0485
19	openai/whisper-tiny	openai-whisper	0.6547	0.3154	0.1083	0.9180	0.0859	-0.1910	0.0802
20	gorkemgoknar/wav2vec2-large-xlsr-53-turkish	transformers_ctc	0.6759	0.2074	0.1230	0.9242	0.0633	-0.1272	0.0758

All models use the same 1,060 general-domain clips. The machine-readable table, including runtime and source counts, is in summary/leaderboard.csv. Per-source WER, CER, and RTF are in summary/source_breakdown.csv.

AcoSemantic-TR

The release also includes ASCS_text, an experimental text-level semantic-affective consistency score. It is reported as an additional analysis dimension and must not be interpreted as a replacement for WER or CER.

For each clip:

ASCS_text = 1 - |sentiment(reference) - sentiment(hypothesis)|

Sentiment scores were produced with lxyuan/distilbert-base-multilingual-cased-sentiments-student. A value near 1 means the reference and hypothesis received similar sentiment scores. This value can remain high even when the hypothesis contains word errors, which is why ASCS is shown alongside WER and CER.

facebook/seamless-m4t-v2-large has the highest mean ASCS_text (0.9609), while openai/whisper-large-v3 has the best WER and a mean ASCS_text of 0.9571.

SER domain-mismatch diagnostic

The earlier audio-level speech-emotion experiment is reported separately because it did not produce a valid model-ranking metric on neutral read speech.

Diagnostic	Result
Evaluated clips	199
Mean maximum emotion confidence	0.255
Maximum confidence	0.270
Clips with confidence above 0.30	0
Matched/mismatched AUC	0.446

The acted-speech SER model collapsed to near-uniform predictions on Common Voice and ISSAI read speech. This negative result motivated the text-based ASCS metric.

Files

• summary/leaderboard.csv: overall WER, CER, RTF, runtime, source counts, and model-level AcoSemantic metrics.
• summary/source_breakdown.csv: metrics by model and source.
• summary/acosemantic_summary.csv: ASCS mean, standard deviation, WER correlation, sentiment means, and sentiment drift.
• data/per_utterance_metrics.csv: de-identified clip-level WER, CER, RTF, and timing.
• data/acosemantic_per_utterance.csv: de-identified clip-level WER, reference sentiment, hypothesis sentiment, and ASCS_text values.

Audio identifiers are retained only to join metrics across tables. No audio or transcript content is included.

Machine-readable field groups

File	Important fields
leaderboard.csv	rank, model, backend, clips, mean_wer, mean_cer, mean_rtf, mean_ascs_text, std_ascs_text, ascs_wer_correlation, mean_sentiment_drift
source_breakdown.csv	model, source, clips, mean_wer, mean_cer, mean_rtf
per_utterance_metrics.csv	model, audio_id, source, wer, cer, rtf, runtime_seconds, audio_seconds
acosemantic_per_utterance.csv	model, audio_id, source, wer, ref_sentiment, hyp_sentiment, ascs_text

Scope and limitations

• These results cover general-domain Turkish speech only.
• Results are hardware- and implementation-dependent, especially RTF.
• Some original model attempts required dependency or loader repairs before the final valid runs.
• Source datasets remain governed by their original licenses and terms.
• This metrics release does not grant rights to redistribute the source audio or transcripts.
• Macro averaging gives short and long clips equal weight; corpus-level edit-count aggregation can produce different WER/CER values.
• Sentiment models can contain language and domain biases. ASCS should be treated as experimental supporting evidence.
• The leaderboard is a snapshot of tested checkpoints and software versions, not a permanent ranking of every Turkish ASR model.

Contributors

• Muhammed Kumcu
• Yagmur Tuncer

Muhammed Kumcu and Yagmur Tuncer jointly carried out the benchmark design, dataset preparation, model execution, metric analysis, documentation, and publication work.

Acknowledgements

We thank Zeynep Zehra Kumcu and Yusuf Uysal for their support.

Citation

@misc{kumcu_tuncer_2026_turkish_asr_benchmark,
  title        = {TurkMedSTT Turkish ASR Benchmark},
  author       = {Kumcu, Muhammed and Tuncer, Yagmur},
  year         = {2026},
  publisher    = {Hugging Face},
  organization = {TurkMedSTT}
}