| --- |
| license: apache-2.0 |
| datasets: |
| - ai4bharat/IndicVoices-ST |
| language: |
| - hi |
| - en |
| metrics: |
| - wer |
| base_model: |
| - nvidia/stt_en_fastconformer_hybrid_large_streaming_multi |
| pipeline_tag: automatic-speech-recognition |
| tags: |
| - Nemo |
| - Hindi |
| - ASR |
| - FastConformer |
| - Conformer |
| - STT |
| --- |
| |
| # Salesken-Streaming-FastConformer-Hindi-ASR |
|
|
| This model is a fine-tuned version of **STT En FastConformer Hybrid Large Streaming** from NVIDIA's NeMo framework, specifically optimized for **Hindi automatic speech recognition (ASR)**. The model has been fine-tuned on the **Aibharath Hindi dataset** to enhance transcription accuracy for Hindi speech, including real-time streaming applications. |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| - **Model type:** Hybrid ASR model (CTC + Attention) |
| - **Model Sample Rate:** 16000 hz |
| - **Language(s) (NLP):** Hindi (`hi`) |
| - **License:** Apache-2.0 |
| - **Finetuned from model:** `nvidia/stt_en_fastconformer_hybrid_large_streaming_multi` |
|
|
|
|
|
|
| ## **How to Get Started with the Model** |
|
|
| You can load and use the model with the following code: |
|
|
| ```python |
| import nemo.collections.asr as nemo_asr |
| asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained(model_name="salesken/Hindi-FastConformer-Streaming-ASR") |
| |
| # Optional: change the default latency. Default latency is 1040ms. Supported latencies: {0: 0ms, 1: 80ms, 16: 480ms, 33: 1040ms}. |
| # Note: These are the worst latency and average latency would be half of these numbers. |
| asr_model.encoder.set_default_att_context_size([70,13]) |
| |
| #Optional: change the default decoder. Default decoder is Transducer (RNNT). Supported decoders: {ctc, rnnt}. |
| asr_model.change_decoding_strategy(decoder_type='rnnt') |
| |
| asr_model.transcribe(['2086-149220-0033.wav']) |
