Automatic Speech Recognition
Transformers
qwen3-asr
latent-reasoning
test-time-compute
parameter-efficient
Instructions to use voidful/latentASR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/latentASR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="voidful/latentASR")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("voidful/latentASR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 4,471 Bytes
262fa3f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | #!/usr/bin/env bash
set -euo pipefail
# Threshold sweep for the LatentASR value-head halting policy.
# Clean audio only, full splits by default, and HuggingFace streaming enabled
# so audio is not materialized under the workspace.
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
source "${SCRIPT_DIR}/lib/common.sh"
ROOT_DIR="$(latent_asr_repo_root "${SCRIPT_DIR}")"
cd "${ROOT_DIR}"
LATENT_CKPT="${LATENT_CKPT:-./latent_qwen_asr_best.pth}"
MODEL_ID="${MODEL_ID:-Qwen/Qwen3-ASR-0.6B}"
PYTHON_BIN="$(latent_asr_python_bin "${PYTHON_BIN:-}")"
MAX_SAMPLES_PER_CONFIG="${MAX_SAMPLES_PER_CONFIG:-0}"
MAX_NEW_TOKENS="${MAX_NEW_TOKENS:-128}"
PRINT_SAMPLES="${PRINT_SAMPLES:-0}"
STREAMING="${STREAMING:-1}"
RUN_BASE="${RUN_BASE:-0}"
CASE_FILTER="${CASE_FILTER:-fleurs_en_us|voxpopuli_en}"
THRESHOLD_SPECS="${THRESHOLD_SPECS:-full:-2.0 neg0p2:-0.2 zero:0.0 pos0p2:0.2 pos0p5:0.5}"
RESUME="${RESUME:-1}"
latent_asr_require_file "${LATENT_CKPT}" "latent checkpoint"
TIMESTAMP="$(date +%Y%m%d_%H%M%S)"
OUT_DIR="${OUT_DIR:-${ROOT_DIR}/eval_runs/threshold_sweep_${TIMESTAMP}}"
LOG_DIR="${OUT_DIR}/logs"
mkdir -p "${LOG_DIR}"
# Format: tag|dataset|configs|split
CASES=(
"fleurs_en_us|google/fleurs|en_us|test"
"voxpopuli_en|facebook/voxpopuli|en|test"
)
EXTRA_ARGS=("$@")
run_case() {
local theta_label="$1"
local theta_value="$2"
local tag="$3"
local dataset="$4"
local configs="$5"
local split="$6"
local json_out="${OUT_DIR}/${tag}_theta_${theta_label}.json"
local log_out="${LOG_DIR}/${tag}_theta_${theta_label}.log"
if [[ "${RESUME}" == "1" && -f "${json_out}" ]]; then
echo "[skip] existing ${json_out}"
return 0
fi
local args=(
--model-id "${MODEL_ID}"
--dataset-name "${dataset}"
--configs "${configs}"
--split "${split}"
--output-json "${json_out}"
--max-samples-per-config "${MAX_SAMPLES_PER_CONFIG}"
--max-new-tokens "${MAX_NEW_TOKENS}"
--latent-ckpt "${LATENT_CKPT}"
--n-latent 4
--num-beams 1
--dynamic-halt-threshold "${theta_value}"
--print-samples "${PRINT_SAMPLES}"
--skip-baseline-ft
--skip-prompt-tuning
--skip-lora-r16
)
if [[ "${RUN_BASE}" != "1" ]]; then
args+=(--skip-base-model)
fi
if [[ "${STREAMING}" == "1" ]]; then
args+=(--streaming)
fi
echo "------------------------------------------------------------"
echo "Case : ${tag}"
echo "Dataset : ${dataset}"
echo "Configs : ${configs}"
echo "Split : ${split}"
echo "Theta : ${theta_label} (${theta_value})"
echo "Streaming : ${STREAMING}"
echo "Run base : ${RUN_BASE}"
echo "JSON : ${json_out}"
echo "LOG : ${log_out}"
"${PYTHON_BIN}" eval.py "${args[@]}" "${EXTRA_ARGS[@]}" 2>&1 | tee "${log_out}"
}
echo "============================================================"
echo "LatentASR Threshold Sweep"
echo "============================================================"
echo "Root dir : ${ROOT_DIR}"
echo "Output dir : ${OUT_DIR}"
echo "Python : ${PYTHON_BIN}"
echo "Model : ${MODEL_ID}"
echo "Latent ckpt : ${LATENT_CKPT}"
echo "Max/config : ${MAX_SAMPLES_PER_CONFIG} (0 = full split)"
echo "Streaming : ${STREAMING}"
echo "Run base : ${RUN_BASE}"
echo "Case filter : ${CASE_FILTER:-(none)}"
echo "Thresholds : ${THRESHOLD_SPECS}"
echo "Extra args : ${EXTRA_ARGS[*]:-(none)}"
echo "============================================================"
SUCCESS=()
FAILED=()
for spec in ${THRESHOLD_SPECS}; do
IFS=':' read -r THETA_LABEL THETA_VALUE <<< "${spec}"
for entry in "${CASES[@]}"; do
IFS='|' read -r TAG DATASET CONFIGS SPLIT <<< "${entry}"
if [[ -n "${CASE_FILTER}" && ! "${TAG}" =~ ${CASE_FILTER} ]]; then
continue
fi
if run_case "${THETA_LABEL}" "${THETA_VALUE}" "${TAG}" "${DATASET}" "${CONFIGS}" "${SPLIT}"; then
SUCCESS+=("${TAG}:${THETA_LABEL}")
else
FAILED+=("${TAG}:${THETA_LABEL}")
fi
done
done
"${PYTHON_BIN}" scripts/summarize_threshold_sweep.py "${OUT_DIR}"
echo ""
echo "============================================================"
echo "Threshold Sweep Summary"
echo "============================================================"
echo "Succeeded (${#SUCCESS[@]}):"
for item in "${SUCCESS[@]}"; do
echo " - ${item}"
done
echo "Failed (${#FAILED[@]}):"
for item in "${FAILED[@]}"; do
echo " - ${item}"
done
echo "Outputs: ${OUT_DIR}"
echo "============================================================"
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