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Datasets:
PatientdeskAI
/
freya-tr-eval-rerun

Tasks:
Text-to-Speech
Languages:
Turkish
Size:
10K<n<100K
Tags:
tts
turkish
benchmark
freya-tr-eval
License:
Dataset card Files
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Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
system: string
take: string
id: string
hyp: string
dur: double
sr: int64
utmos: double
antalia-1: struct<takes: list<item: struct<take: string, n: int64, wer: double, cer: double, clean: double, utm (... 285 chars omitted)
  child 0, takes: list<item: struct<take: string, n: int64, wer: double, cer: double, clean: double, utmos: double, au (... 30 chars omitted)
      child 0, item: struct<take: string, n: int64, wer: double, cer: double, clean: double, utmos: double, audio_s: doub (... 18 chars omitted)
          child 0, take: string
          child 1, n: int64
          child 2, wer: double
          child 3, cer: double
          child 4, clean: double
          child 5, utmos: double
          child 6, audio_s: double
          child 7, silent: int64
  child 1, n_takes: int64
  child 2, wer: double
  child 3, wer_sd: double
  child 4, wer_min: double
  child 5, wer_max: double
  child 6, cer: double
  child 7, cer_sd: double
  child 8, clean: double
  child 9, utmos: double
  child 10, always_right: int64
  child 11, always_wrong: int64
  child 12, rtf: double
  child 13, rtf_n: int64
  child 14, gpu: string
  child 15, sample_rate: int64
alania-2-f1-model: struct<takes: list<item: struct<take: string, n: int64, wer: double, cer: double, clean: double, utm (... 285 chars omitted)
  child 0, takes: list<item: struct<take: string, n: int64, wer: double, cer: double, clean: double, utmos: double, au (... 30 chars omitted)
      child 0, item: struct<take: string, n: int64, 
...
hild 7, silent: int64
  child 1, n_takes: int64
  child 2, wer: double
  child 3, wer_sd: double
  child 4, wer_min: double
  child 5, wer_max: double
  child 6, cer: double
  child 7, cer_sd: double
  child 8, clean: double
  child 9, utmos: double
  child 10, always_right: int64
  child 11, always_wrong: int64
  child 12, rtf: null
  child 13, rtf_n: null
  child 14, gpu: null
  child 15, sample_rate: int64
ema-lightning-compiled: struct<takes: list<item: struct<take: string, n: int64, wer: double, cer: double, clean: double, utm (... 285 chars omitted)
  child 0, takes: list<item: struct<take: string, n: int64, wer: double, cer: double, clean: double, utmos: double, au (... 30 chars omitted)
      child 0, item: struct<take: string, n: int64, wer: double, cer: double, clean: double, utmos: double, audio_s: doub (... 18 chars omitted)
          child 0, take: string
          child 1, n: int64
          child 2, wer: double
          child 3, cer: double
          child 4, clean: double
          child 5, utmos: double
          child 6, audio_s: double
          child 7, silent: int64
  child 1, n_takes: int64
  child 2, wer: double
  child 3, wer_sd: double
  child 4, wer_min: double
  child 5, wer_max: double
  child 6, cer: double
  child 7, cer_sd: double
  child 8, clean: double
  child 9, utmos: double
  child 10, always_right: int64
  child 11, always_wrong: int64
  child 12, rtf: double
  child 13, rtf_n: int64
  child 14, gpu: string
  child 15, sample_rate: int64
to
{'alania-1-model': {'takes': List({'take': Value('string'), 'n': Value('int64'), 'wer': Value('float64'), 'cer': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'audio_s': Value('float64'), 'silent': Value('int64')}), 'n_takes': Value('int64'), 'wer': Value('float64'), 'wer_sd': Value('float64'), 'wer_min': Value('float64'), 'wer_max': Value('float64'), 'cer': Value('float64'), 'cer_sd': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'always_right': Value('int64'), 'always_wrong': Value('int64'), 'rtf': Value('float64'), 'rtf_n': Value('int64'), 'gpu': Value('string'), 'sample_rate': Value('int64')}, 'alania-1': {'takes': List({'take': Value('string'), 'n': Value('int64'), 'wer': Value('float64'), 'cer': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'audio_s': Value('float64'), 'silent': Value('int64')}), 'n_takes': Value('int64'), 'wer': Value('float64'), 'wer_sd': Value('float64'), 'wer_min': Value('float64'), 'wer_max': Value('float64'), 'cer': Value('float64'), 'cer_sd': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'always_right': Value('int64'), 'always_wrong': Value('int64'), 'rtf': Value('null'), 'rtf_n': Value('null'), 'gpu': Value('null'), 'sample_rate': Value('int64')}, 'alania-2-f1-model': {'takes': List({'take': Value('string'), 'n': Value('int64'), 'wer': Value('float64'), 'cer': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'audio_s': V
...
 'utmos': Value('float64'), 'always_right': Value('int64'), 'always_wrong': Value('int64'), 'rtf': Value('float64'), 'rtf_n': Value('int64'), 'gpu': Value('string'), 'sample_rate': Value('int64')}, 'trendyol-tts': {'takes': List({'take': Value('string'), 'n': Value('int64'), 'wer': Value('float64'), 'cer': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'audio_s': Value('float64'), 'silent': Value('int64')}), 'n_takes': Value('int64'), 'wer': Value('float64'), 'wer_sd': Value('float64'), 'wer_min': Value('float64'), 'wer_max': Value('float64'), 'cer': Value('float64'), 'cer_sd': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'always_right': Value('int64'), 'always_wrong': Value('int64'), 'rtf': Value('float64'), 'rtf_n': Value('int64'), 'gpu': Value('string'), 'sample_rate': Value('int64')}, 'xtts-v2': {'takes': List({'take': Value('string'), 'n': Value('int64'), 'wer': Value('float64'), 'cer': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'audio_s': Value('float64'), 'silent': Value('int64')}), 'n_takes': Value('int64'), 'wer': Value('float64'), 'wer_sd': Value('float64'), 'wer_min': Value('float64'), 'wer_max': Value('float64'), 'cer': Value('float64'), 'cer_sd': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'always_right': Value('int64'), 'always_wrong': Value('int64'), 'rtf': Value('float64'), 'rtf_n': Value('int64'), 'gpu': Value('string'), 'sample_rate': Value('int64')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              system: string
              take: string
              id: string
              hyp: string
              dur: double
              sr: int64
              utmos: double
              antalia-1: struct<takes: list<item: struct<take: string, n: int64, wer: double, cer: double, clean: double, utm (... 285 chars omitted)
                child 0, takes: list<item: struct<take: string, n: int64, wer: double, cer: double, clean: double, utmos: double, au (... 30 chars omitted)
                    child 0, item: struct<take: string, n: int64, wer: double, cer: double, clean: double, utmos: double, audio_s: doub (... 18 chars omitted)
                        child 0, take: string
                        child 1, n: int64
                        child 2, wer: double
                        child 3, cer: double
                        child 4, clean: double
                        child 5, utmos: double
                        child 6, audio_s: double
                        child 7, silent: int64
                child 1, n_takes: int64
                child 2, wer: double
                child 3, wer_sd: double
                child 4, wer_min: double
                child 5, wer_max: double
                child 6, cer: double
                child 7, cer_sd: double
                child 8, clean: double
                child 9, utmos: double
                child 10, always_right: int64
                child 11, always_wrong: int64
                child 12, rtf: double
                child 13, rtf_n: int64
                child 14, gpu: string
                child 15, sample_rate: int64
              alania-2-f1-model: struct<takes: list<item: struct<take: string, n: int64, wer: double, cer: double, clean: double, utm (... 285 chars omitted)
                child 0, takes: list<item: struct<take: string, n: int64, wer: double, cer: double, clean: double, utmos: double, au (... 30 chars omitted)
                    child 0, item: struct<take: string, n: int64, 
              ...
              hild 7, silent: int64
                child 1, n_takes: int64
                child 2, wer: double
                child 3, wer_sd: double
                child 4, wer_min: double
                child 5, wer_max: double
                child 6, cer: double
                child 7, cer_sd: double
                child 8, clean: double
                child 9, utmos: double
                child 10, always_right: int64
                child 11, always_wrong: int64
                child 12, rtf: null
                child 13, rtf_n: null
                child 14, gpu: null
                child 15, sample_rate: int64
              ema-lightning-compiled: struct<takes: list<item: struct<take: string, n: int64, wer: double, cer: double, clean: double, utm (... 285 chars omitted)
                child 0, takes: list<item: struct<take: string, n: int64, wer: double, cer: double, clean: double, utmos: double, au (... 30 chars omitted)
                    child 0, item: struct<take: string, n: int64, wer: double, cer: double, clean: double, utmos: double, audio_s: doub (... 18 chars omitted)
                        child 0, take: string
                        child 1, n: int64
                        child 2, wer: double
                        child 3, cer: double
                        child 4, clean: double
                        child 5, utmos: double
                        child 6, audio_s: double
                        child 7, silent: int64
                child 1, n_takes: int64
                child 2, wer: double
                child 3, wer_sd: double
                child 4, wer_min: double
                child 5, wer_max: double
                child 6, cer: double
                child 7, cer_sd: double
                child 8, clean: double
                child 9, utmos: double
                child 10, always_right: int64
                child 11, always_wrong: int64
                child 12, rtf: double
                child 13, rtf_n: int64
                child 14, gpu: string
                child 15, sample_rate: int64
              to
              {'alania-1-model': {'takes': List({'take': Value('string'), 'n': Value('int64'), 'wer': Value('float64'), 'cer': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'audio_s': Value('float64'), 'silent': Value('int64')}), 'n_takes': Value('int64'), 'wer': Value('float64'), 'wer_sd': Value('float64'), 'wer_min': Value('float64'), 'wer_max': Value('float64'), 'cer': Value('float64'), 'cer_sd': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'always_right': Value('int64'), 'always_wrong': Value('int64'), 'rtf': Value('float64'), 'rtf_n': Value('int64'), 'gpu': Value('string'), 'sample_rate': Value('int64')}, 'alania-1': {'takes': List({'take': Value('string'), 'n': Value('int64'), 'wer': Value('float64'), 'cer': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'audio_s': Value('float64'), 'silent': Value('int64')}), 'n_takes': Value('int64'), 'wer': Value('float64'), 'wer_sd': Value('float64'), 'wer_min': Value('float64'), 'wer_max': Value('float64'), 'cer': Value('float64'), 'cer_sd': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'always_right': Value('int64'), 'always_wrong': Value('int64'), 'rtf': Value('null'), 'rtf_n': Value('null'), 'gpu': Value('null'), 'sample_rate': Value('int64')}, 'alania-2-f1-model': {'takes': List({'take': Value('string'), 'n': Value('int64'), 'wer': Value('float64'), 'cer': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'audio_s': V
              ...
               'utmos': Value('float64'), 'always_right': Value('int64'), 'always_wrong': Value('int64'), 'rtf': Value('float64'), 'rtf_n': Value('int64'), 'gpu': Value('string'), 'sample_rate': Value('int64')}, 'trendyol-tts': {'takes': List({'take': Value('string'), 'n': Value('int64'), 'wer': Value('float64'), 'cer': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'audio_s': Value('float64'), 'silent': Value('int64')}), 'n_takes': Value('int64'), 'wer': Value('float64'), 'wer_sd': Value('float64'), 'wer_min': Value('float64'), 'wer_max': Value('float64'), 'cer': Value('float64'), 'cer_sd': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'always_right': Value('int64'), 'always_wrong': Value('int64'), 'rtf': Value('float64'), 'rtf_n': Value('int64'), 'gpu': Value('string'), 'sample_rate': Value('int64')}, 'xtts-v2': {'takes': List({'take': Value('string'), 'n': Value('int64'), 'wer': Value('float64'), 'cer': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'audio_s': Value('float64'), 'silent': Value('int64')}), 'n_takes': Value('int64'), 'wer': Value('float64'), 'wer_sd': Value('float64'), 'wer_min': Value('float64'), 'wer_max': Value('float64'), 'cer': Value('float64'), 'cer_sd': Value('float64'), 'clean': Value('float64'), 'utmos': Value('float64'), 'always_right': Value('int64'), 'always_wrong': Value('int64'), 'rtf': Value('float64'), 'rtf_n': Value('int64'), 'gpu': Value('string'), 'sample_rate': Value('int64')}}
              because column names don't match

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Freya-TR-Eval, re-run

Scripts and per-sentence results from our re-run of Freya-TR-Eval on Turkish text-to-speech systems, with one harness and the protocol published with EMA Lightning: all 495 sentences, three takes per system, an 8 kHz band, faster-whisper large-v3 at beam 5, and EMA's score_norm. Speed was measured on one NVIDIA L4 per system.

Charts, leaderboard and audio samples: https://speech.patientdesk.ai/en/benchmarks (Turkish: https://speech.patientdesk.ai/benchmarks)

Run on 7 October 2026 by PatientDesk AI. Alania-2 and Alania-1 are our commercial models; Antalia-1 is our open-weights release.

Results

# System Params WER % CER % Clean UTMOS RTF (L4) Source
1 EMA Lightning (plain PyTorch) 8.6M 1.04 ± 0.17 0.22 93.7% 3.30 0.017 ours
2 EMA Lightning (compiled) 8.6M 1.04 ± 0.17 0.22 93.7% 3.30 0.0030 ours
3 Trendyol-TTS (cfg 2.0, 16 steps) 2.38B 1.16 ± 0.16 0.23 93.1% 3.82 0.962 ours
4 Gemini 3.8 Flash TTS (voice Kore) – 1.23 ± 0.10 0.29 92.9% 3.53 – ours
5 Gemini 3.8 Flash-Lite TTS (voice Kore) – 1.34 ± 0.19 0.27 91.9% 3.63 – ours
6 Anka TTS (built-in male voice) 336M 1.42 ± 0.06 0.30 91.6% 3.52 0.322 ours
7 ElevenLabs v4 (voice George) – 1.43 ± 0.12 0.27 – – – quoted: EMA Lightning card
8 Alania-2 (production API, voice F1) 488M 1.44 ± 0.15 0.28 91.7% 3.67 – ours
9 Alania-2 (model only, no serving checks) 488M 1.51 ± 0.19 0.30 91.8% 3.66 0.151 ours
10 ElevenLabs v4 Turbo (voice George) – 1.54 ± 0.22 0.29 – – – quoted: EMA Lightning card
11 Chatterbox Multilingual (Anka male reference) 500M 1.91 ± 0.35 0.43 88.8% 3.51 2.892 ours
12 Piper (tr_TR dfki medium, CPU) 16M 3.41 ± 0.21 0.73 79.4% 3.71 0.038 ours
13 XTTS-v2 (Anka male reference) 470M 3.67 ± 0.37 1.50 77.1% 2.97 0.455 ours
14 Alania-1 (production API) 3.3B 5.99 ± 0.47 1.81 67.3% 3.19 – ours
15 Antalia-1 (open weights, release recipe) 305M 6.28 ± 0.27 2.49 66.7% 2.72 0.476 ours
16 MMS-TTS (tur) 36M 6.38 ± 0.19 1.48 65.7% 3.79 0.014 ours
17 Coqui GlowTTS (tr common-voice, lowercased input) 28M 10.93 ± 0.00 2.96 46.1% 3.09 0.015 ours
18 FreyaTTS-small (default voice) 183M 12.02 ± 0.00 4.95 47.1% 2.81 0.286 ours
19 F5-TTS base (no Turkish training) 336M 77.84 ± 0.13 32.58 – – – quoted: Anka TTS card

WER/CER: corpus level per take after score_norm, mean ± sample standard deviation over three takes. Clean: share of sentences with no word error. UTMOS: UTMOS22 strong on full-band audio (English-trained; a rough guide only). RTF: generation time / audio length on one NVIDIA L4, middle of three passes after a warm-up; API rows have none. Alania-1's RTF (2.49 on the L4, model only) was measured on the first 100 sentences. Chatterbox Multilingual slowed down from pass to pass (RTF 1.69, 2.89, 4.21) with no change in output length; as for every system, the middle pass is reported. Rows marked quoted were not run by us.

What is in this repo

Path What
harness/runner.py Shared harness: warm-up, three seeded passes (sentence i of seed s uses seed 100000·s + i), WAV output, timing
harness/systems/run_*.py One adapter per open system
harness/synth_api.py Alania (our public API) and Gemini 3.8 TTS (Vertex AI), three independent requests per sentence
harness/score.py Transcription as in EMA's eval.py, plus UTMOS
harness/aggregate.py WER/CER per take, mean ± std, clean rate, UTMOS, RTF → results.json
harness/ours/ Alania-1 and Alania-2 run offline for the model-only rows; they need our private serving code and weights, and are here so the settings can be read
results/scores/<system>.jsonl Every clip: system, take, sentence id, Whisper transcript, duration, sample rate, UTMOS
results/timing/<system>.json Per-pass generation time and audio length, GPU name
results/results.json The aggregated numbers above

Audio is not included: several systems' outputs are under non-commercial licences. Everything can be regenerated with the scripts.

Reproduce

Each system gets its own virtual environment (their dependencies conflict). Set BENCH_ROOT to a working directory; the sentences are fetched from the Hub.

System Install Run
EMA Lightning 1.0.1 pip install ema-lightning soundfile run_ema.py plain / run_ema.py compiled
Trendyol-TTS pip install voxcpm soundfile run_trendyol.py
Anka TTS v0.1 pip install "anka-tts[tts]" "pyarrow<17" soundfile run_anka.py
Chatterbox Multilingual pip install chatterbox-tts "setuptools<81" soundfile REF_WAV=ref/male.wav run_chatterbox.py
XTTS-v2, GlowTTS pip install coqui-tts "transformers<4.50" torchcodec soundfile run_xtts.py, run_glow.py
MMS-TTS pip install torch transformers soundfile run_mms.py
Piper pip install piper-tts soundfile, voice tr/tr_TR/dfki/medium from rhasspy/piper-voices PIPER_MODEL=... run_piper.py
FreyaTTS-small clone github.com/freyavoiceai/FreyaTTS, pip install -r requirements.txt FREYATTS_SRC=... run_freya.py
Antalia-1 clone github.com/0daycloud/antalia, uv sync; BigVGAN at commit 7d2b454 with the repo's patch, plus matplotlib librosa PYTHONPATH=bigvgan run_antalia.py
Scoring pip install faster-whisper librosa soundfile torch torchaudio nvidia-cublas-cu12 "nvidia-cudnn-cu12==9.*" score.py <system>... then aggregate.py

The voice reference for Chatterbox and XTTS-v2 is Anka TTS's packaged male clip (anka/data/voices/male.wav), as in the Anka TTS report. Coqui's Turkish GlowTTS has no capital letters in its symbol set and drops them silently, so run_glow.py lowercases its input the Turkish way (I→ı, İ→i); without that every sentence loses its first letter (about 21% WER).

Credits

Benchmark: Freya-TR-Eval by Freya (CC-BY-4.0). Protocol, score_norm and the transcription recipe: EMA Lightning's card and eval.py (Apache-2.0). Baseline settings for the cloning systems: the Anka TTS report. Quoted rows: ElevenLabs v4 and v4 Turbo from the EMA Lightning card; F5-TTS base from the Anka TTS card.
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