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