Instructions to use typhoon-ai/typhoon-asr-streaming-115m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use typhoon-ai/typhoon-asr-streaming-115m with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("typhoon-ai/typhoon-asr-streaming-115m") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Typhoon ASR Streaming 115M
Cache-aware streaming Thai ASR (FastConformer-Transducer, 115M params). Converted from Typhoon ASR Real-time โ causal convolutions + chunked limited-context attention swapped in, every weight copied as a warm start, then briefly fine-tuned on the ~11k-hour Thai corpus. Unlike the full-context base (which collapses to 62.8% CER when forced to stream at 1040 ms), this model decodes live, chunk by chunk, with the encoder cache carried across chunks.
At a glance
| Architecture | FastConformer-Transducer (RNN-T), cache-aware streaming |
| NeMo class | EncDecRNNTBPEModel |
| Tokenizer | single 2048-token Thai BPE |
| Latency knob | chunk_size ร 80 ms โ 1040 / 480 / 80 ms |
Multi-latency att_context_size |
[[70,13],[70,6],[70,1],[70,0]] |
| Streaming CER (TVSpeech) | 19.4% @1040 ms ยท 16.4% @3200 ms |
| RTF (batch-1, H100 @1040 ms) | 0.020 (~50ร real time) |
| Paired n-gram | typhoon-ai/typhoon-asr-streaming-115m-ngram |
Use this model for low latency / low cost when English code-switching is light; for best
accuracy use
typhoon-ai/typhoon-asr-streaming-nemotron-0.6b.
Usage
Requires NVIDIA NeMo with cache-aware streaming support (see the project repo for the pinned commit and the full streaming loop / server / demo):
import nemo.collections.asr as nemo_asr
model = nemo_asr.models.ASRModel.restore_from("typhoon-asr-streaming-115m.nemo",
map_location="cuda")
model.eval()
# offline (whole file)
text = model.transcribe(audio=["clip.wav"])[0].text
# true streaming: model.encoder.setup_streaming_params(chunk_size=13, left_chunks=2,
# shift_size=13) + conformer_stream_step(...) โ see demo/asr_engine.py in the repo.
Decode-time vocabulary steering (phrase boosting + n-gram shallow fusion) runs inside the
streaming decoder โ see the repo's docs/SHALLOW_FUSION.md.
License & attribution
Released under CC-BY-4.0, following the base-model chain:
- fine-tuned + converted from scb10x/typhoon-asr-realtime (CC-BY-4.0, SCB DataX),
- itself fine-tuned from nvidia/stt_en_fastconformer_transducer_large (CC-BY-4.0, NVIDIA).
Modifications in this release: full-context โ cache-aware streaming conversion (causal
convolutions, chunked limited-context attention, multi-latency att_context_size) and a
1-epoch Thai fine-tune on the Typhoon ASR Real-time corpus.
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Model tree for typhoon-ai/typhoon-asr-streaming-115m
Base model
nvidia/stt_en_fastconformer_transducer_large