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Shravya is free for research and other non-commercial use. We read every request and approve by hand, usually within a few days. Tell us briefly who you are and what you plan to do with the model.

Shravya (श्रव्य)

Nepali speech recognition by Fiste AI. Speech in, Devanagari text out.

Accurate. 23.5% word error rate averaged over five test sets, the lowest of the 16 Nepali systems we measured.
Made for real speech. Conversations, phone calls, podcasts and Nepali with English mixed in.
Light. One Python file, no graphics card, 28 times faster than real time on a desktop processor.
Live. Shravya Live writes the text while someone is still speaking. Preview.

Benchmark

Word error rate in percent, lower is better.

Model	Size	Mean	FLEURS	IndicVoices	IndicVoices (phone)	Podcast	Code-switch
Shravya 0.1	120M	23.5	23.9	11.8	13.8	34.1	33.7
IndicConformer 600M	600M	27.3	23.7	15.0	17.9	41.7	38.4
IndicConformer Nepali	129M	27.7	24.8	11.3	13.6	48.8	40.1
SraVaani 1.0	430M	29.7	25.9	13.9	19.1	45.0	44.5
Kriti Telephony	129M	31.2	31.8	25.1	27.2	28.3	43.5
IndicConformer Nepali int8	120M	33.3	29.2	16.3	21.3	54.5	45.1
SraVaani code-mix (AI Lab)	449M	38.2	34.5	34.0	39.6	39.3	43.6
IndicConformer code-mix (AI Lab)	128M	38.6	34.9	34.3	38.4	42.1	43.2
Whisper turbo code-mix (AI Lab)	809M	42.0	39.2	39.3	46.9	39.8	44.9
Ratna (Ampixa Labs)	32M	54.2	56.9	48.3	53.9	46.0	65.6

Shravya was trained on speech like these five test sets, and most of the other systems were not. All 16 systems, three more test sets and the method are in the full benchmark.

Use it

Request access with the form at the top of this page. We approve by hand. Then:

pip install onnxruntime numpy soundfile soxr huggingface_hub
hf auth login
hf download fiste-ai/shravya --local-dir shravya-model
python shravya-model/shravya.py clip.wav

import sys
sys.path.insert(0, "shravya-model")
import shravya

model = shravya.load()
print(model.transcribe("clip.wav"))                      # standard spelling
print(model.transcribe("call.wav", style="verbatim"))    # the way it was said

Shravya Live (preview)

Shravya Live writes the text while someone is still speaking. When the speaker pauses it reads the phrase once more as a whole, and that is the text to keep. It is a second model, in the folder live/ of the same download.

model = shravya.load("shravya-model/live")
stream = model.stream()                    # text every 640 ms; chunk_ms=320 answers sooner
for block in microphone:                   # 16 kHz mono float samples, blocks of any length
    print(stream.feed(block))              # the phrase so far
    if stream.pause > 0.8:                 # the speaker has stopped for a moment
        print(stream.cut())                # the phrase read once more: the text to keep
print(stream.finish())                     # the same for the last phrase

To try it on a recording: python shravya-model/shravya.py --model shravya-model/live --stream clip.wav

Word error rate in percent on the five test sets above, lower is better.

Shravya Live	Mean	FLEURS	IndicVoices	IndicVoices (phone)	Podcast	Code-switch
Live text, every 640 ms	28.4	31.4	17.1	21.4	35.3	36.8
Live text, every 320 ms	31.8	35.7	19.6	25.7	38.1	39.5
Final text of a phrase	23.5	24.5	13.6	16.1	30.9	32.5

On one thread of a desktop processor it runs 8 times faster than real time (5.7 times at 320 ms). It is a preview: measured on recordings fed to it in small blocks, not yet on live calls.

Details


Version	0.1, 11 November 2026
Model	Conformer transducer, 119.6M parameters, 500 MB
Input	any sound file; long recordings are cut at quiet points
Output	Devanagari without punctuation, in `standard` or `verbatim` spelling
Speed	28 times faster than real time on 8 processor threads, 1.2 GB of memory
Training	fine-tuned on 174 hours of Nepali speech
Shravya Live	0.1 (preview), 13 November 2026: 512 MB in `live/`, text every 640 or 320 ms, 1.7 GB of memory
Licence	weights CC BY-NC 4.0, code MIT

Limits. No punctuation, and English words come out in Devanagari. Phone calls are still hard (38.2% word error rate on real calls), so check names and numbers. Shravya Live is a preview, and the text it writes while someone is speaking has more errors than its final text.

Using it at work. These weights are non-commercial. For a product or a paid service, a demo on your own recordings, or a pilot in a call centre or a newsroom, write to us: request a demo or fistelabs@gmail.com.

श्रव्य (shravya) means audible. Website · Code

Acknowledgements and licences

Shravya is fine-tuned from the Nepali IndicConformer by AI4Bharat (MIT License, see NOTICE.md).
Training speech: IndicVoices and FLEURS (CC BY 4.0), saileshbro/nepali-cs-asr (CC BY-NC 4.0), Bijay13/nepali-podcast-code-switch-asr-dataset, and telephone speech that is not distributed. That mix is why the weights are non-commercial and by request.
English words in training transcripts were transliterated with the word map of Ampixa/rani-asr (MIT). Some verbatim spellings follow Kriti Telephony (MIT).
The benchmark names the systems it measures, among them Ratna (Ampixa Labs) and Rani (Ampixa Labs).

@misc{fisteai2026shravya,
  title  = {Shravya: Nepali speech recognition},
  author = {{Fiste AI}},
  year   = {2026},
  url    = {https://huggingface.co/fiste-ai/shravya}
}

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Datasets used to train fiste-ai/shravya

Evaluation results

WER on FLEURS (Nepali, test)
test set self-reported

23.900
CER on FLEURS (Nepali, test)
test set self-reported

8.200
WER on NepTel 0.2 (telephone calls)
self-reported

38.200