#!/bin/bash
# Phase 1: Zipformer-M CTC streaming (causal), pure CTC, from scratch, char vocab.
set -e
source /workspace/venvs/icefall/bin/activate
export PYTHONPATH=/root/icefall:$PYTHONPATH
cd /root/icefall/egs/hindi/ASR

ROOT=/workspace/hindi_ft/asr_ctc
python zipformer/train.py \
  --world-size 1 \
  --num-epochs 30 \
  --start-epoch 1 \
  --exp-dir $ROOT/exp_p1 \
  --lang-dir $ROOT/data/lang_char \
  --manifest-dir $ROOT/data/fbank \
  --use-ctc 1 --use-transducer 0 --ctc-loss-scale 1.0 \
  --causal 1 \
  --chunk-size "16,32,64,-1" \
  --left-context-frames "64,128,256,-1" \
  --max-duration 300 \
  --use-fp16 0 \
  --num-workers 8 \
  --enable-musan False \
  --on-the-fly-feats True \
  --base-lr 0.045   # recipe default; bf16 makes it stable. 0.03 was too low -> CTC blank collapse.