SODA Discrete Audio Models
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SODA Discrete Audio Models
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potsawee/soda-demo:Thanks a Million for This HF Space!
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Thanks a Million for This HF Space!
#1 opened 3 days ago
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9voltfan2009
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fabiosuizu's
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Hi everyone!
I've been working on a pronunciation assessment engine optimized for edge deployment and real-time feedback. Wanted to share it with the community and get feedback.
**What it does**: Scores English pronunciation at 4 levels of granularity — phoneme, word, sentence, and overall (0-100 each). Returns IPA and ARPAbet notation for every phoneme.
**Key specs**:
- 17MB total model size (NeMo Citrinet-256, INT4 quantized)
- 257ms median inference on CPU
- Exceeds human inter-annotator agreement at phone-level (+4.5%) and sentence-level (+5.2%)
- Benchmarked on speechocean762 (2,500 test utterances)
- Tested across 7 L1 backgrounds (Chinese, Japanese, Korean, Arabic, Spanish, Vietnamese, Russian)
**Architecture**: CTC forced alignment + Viterbi decoding + GOP (Goodness of Pronunciation) scoring + MLP/XGBoost ensemble heads. No wav2vec2 dependency — the entire pipeline runs in 17MB.
**Try it**: fabiosuizu/pronunciation-assessment
The demo lets you record audio or upload a file, enter the expected text, and get instant scoring down to individual phonemes.
**API access**: Available via REST API, MCP servers (for AI agents), and Azure Marketplace. Details in the Space description.
Would love feedback on:
1. Use cases you'd find this useful for
2. Languages you'd want supported next
3. Whether the scoring feels calibrated for your experience level
Thanks!
I've been working on a pronunciation assessment engine optimized for edge deployment and real-time feedback. Wanted to share it with the community and get feedback.
**What it does**: Scores English pronunciation at 4 levels of granularity — phoneme, word, sentence, and overall (0-100 each). Returns IPA and ARPAbet notation for every phoneme.
**Key specs**:
- 17MB total model size (NeMo Citrinet-256, INT4 quantized)
- 257ms median inference on CPU
- Exceeds human inter-annotator agreement at phone-level (+4.5%) and sentence-level (+5.2%)
- Benchmarked on speechocean762 (2,500 test utterances)
- Tested across 7 L1 backgrounds (Chinese, Japanese, Korean, Arabic, Spanish, Vietnamese, Russian)
**Architecture**: CTC forced alignment + Viterbi decoding + GOP (Goodness of Pronunciation) scoring + MLP/XGBoost ensemble heads. No wav2vec2 dependency — the entire pipeline runs in 17MB.
**Try it**: fabiosuizu/pronunciation-assessment
The demo lets you record audio or upload a file, enter the expected text, and get instant scoring down to individual phonemes.
**API access**: Available via REST API, MCP servers (for AI agents), and Azure Marketplace. Details in the Space description.
Would love feedback on:
1. Use cases you'd find this useful for
2. Languages you'd want supported next
3. Whether the scoring feels calibrated for your experience level
Thanks!
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Thanks a Million for This HF Space! + Voice Cloning Option?
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#2 opened 6 days ago
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9voltfan2009
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Thanks a Million for This HF Space!
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#1 opened 12 days ago
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9voltfan2009
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You can now run MiniMax-2.5 locally! 🚀
At 230B parameters, MiniMax-2.5 is the strongest LLM under 700B params, delivering SOTA agentic coding & chat.
Run Dynamic 3/4-bit on a 128GB Mac for 20 tokens/s.
Guide: https://unsloth.ai/docs/models/minimax-2.5
GGUF: unsloth/MiniMax-M2.5-GGUF
At 230B parameters, MiniMax-2.5 is the strongest LLM under 700B params, delivering SOTA agentic coding & chat.
Run Dynamic 3/4-bit on a 128GB Mac for 20 tokens/s.
Guide: https://unsloth.ai/docs/models/minimax-2.5
GGUF: unsloth/MiniMax-M2.5-GGUF
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Research, Con, and a Feature
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#2 opened 11 days ago
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9voltfan2009
Does This Space Support English?
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#1 opened 12 days ago
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9voltfan2009
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test
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