Automatic Speech Recognition
Transformers
Safetensors
Panjabi
whisper
asr
gurbani
gurmukhi
punjabi
sehaj-path
kirtan
Eval Results (legacy)
Instructions to use surindersinghssj/surt-small-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use surindersinghssj/surt-small-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="surindersinghssj/surt-small-v3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("surindersinghssj/surt-small-v3") model = AutoModelForSpeechSeq2Seq.from_pretrained("surindersinghssj/surt-small-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update training data description to 'publicly available recordings with aligned transcripts'
Browse files
README.md
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@@ -118,9 +118,9 @@ model.generation_config.forced_decoder_ids = None
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| Source | Repo | Hours | Role |
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| New sehaj (
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| Old sehaj (studio) | [`gurbani-sehajpath`](https://huggingface.co/datasets/surindersinghssj/gurbani-sehajpath) | ~66h | Extra sehaj stream |
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| Kirtan (
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Training mix: **~220h sehaj** (36%) **+ ~420h kirtan** (64%) via oversampling aux at `AUX_TRAIN_PROBABILITY=0.64`. Text column normalized to `final_text` on all canonical sources. Old sehaj's `gurmukhi_text` column renamed at load.
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- Not trained on **non-Gurbani Punjabi speech** — expect degradation on everyday Punjabi conversation
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- Not trained on **English / Hindi / other languages** — base Whisper multilingual ability is mostly lost after full fine-tune
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- **Speaker / mic / recording bias** — training data skews toward
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- **Katha (spoken commentary)** is not in the training distribution — quality will vary
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## How this compares to prior Surt versions
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| Source | Repo | Hours | Role |
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| New sehaj (publicly available recordings with aligned transcripts) | [`gurbani-sehajpath-yt-captions-canonical`](https://huggingface.co/datasets/surindersinghssj/gurbani-sehajpath-yt-captions-canonical) | ~160h | Primary sehaj stream |
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| Old sehaj (studio) | [`gurbani-sehajpath`](https://huggingface.co/datasets/surindersinghssj/gurbani-sehajpath) | ~66h | Extra sehaj stream |
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| Kirtan (publicly available recordings with aligned transcripts) | [`gurbani-kirtan-yt-captions-300h-canonical`](https://huggingface.co/datasets/surindersinghssj/gurbani-kirtan-yt-captions-300h-canonical) | ~420h | Aux kirtan stream |
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Training mix: **~220h sehaj** (36%) **+ ~420h kirtan** (64%) via oversampling aux at `AUX_TRAIN_PROBABILITY=0.64`. Text column normalized to `final_text` on all canonical sources. Old sehaj's `gurmukhi_text` column renamed at load.
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- Not trained on **non-Gurbani Punjabi speech** — expect degradation on everyday Punjabi conversation
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- Not trained on **English / Hindi / other languages** — base Whisper multilingual ability is mostly lost after full fine-tune
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- **Speaker / mic / recording bias** — training data skews toward publicly available web-sourced recordings with aligned transcripts; live Darbar Sahib PA reverb or noisy field recordings may WER worse
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- **Katha (spoken commentary)** is not in the training distribution — quality will vary
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## How this compares to prior Surt versions
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