README.md

metadata

title: Reading & Pronunciation Coach
emoji: 🗣️
colorFrom: indigo
colorTo: green
sdk: gradio
app_file: app.py
pinned: false
short_description: Hindi & English reading practice with pronunciation scoring

Reading & Pronunciation Coach

Read a passage aloud in Hindi or English and get a word-level error breakdown: WER, CER, strict and lenient accuracy, speaking rate, and pause count.

Configuration

Set these under Settings → Variables and secrets.

Name	Type	Value
ASR_BACKEND	Variable	groq | zerogpu | local | auto
GROQ_API_KEY	Secret	required for the groq backend
GROQ_MODEL_HI	Variable	default whisper-large-v3
GROQ_MODEL_EN	Variable	default whisper-large-v3-turbo
LOCAL_TIER	Variable	fast | balanced | accurate (local only)

Choosing a backend

groq on free CPU hardware — recommended. Only transcription needs a GPU, and Groq rents it per second of audio. Uncomment nothing in requirements.txt. Trade-off: no per-word confidence, so the "Unclear words" metric is hidden.

zerogpu — free GPU, transformers path. Uncomment the ZeroGPU block in requirements.txt and select ZeroGPU hardware. Note that faster-whisper will not work here: CTranslate2 does not allocate through PyTorch's CUDA allocator, so it cannot see a ZeroGPU-assigned device.

local — your machine or paid GPU Spaces hardware. The only backend that reports per-word ASR confidence, which is a useful mumbling signal. Uncomment the faster-whisper line.

Local development

pip install -r requirements.txt faster-whisper
ASR_BACKEND=local LOCAL_TIER=fast python app.py