acft-whisper-small.da

Danish Whisper (FUTO / ACFT) — selected components

This document covers four pieces of the pipeline: training (train_acft.py), Hugging Face → GGML conversion (convert-to-android.sh), the best small checkpoint (small_244M_danish_whisper_acft_futo_best), and Android-ready binaries (android-futo-ready).

Model Card

Fine-tuned OpenAI Whisper checkpoints for dynamic audio context robustness (ACFT): shorter encoder contexts work well on short or streaming audio. Method and upstream models: FUTO whisper-acft. This repo trains Danish variants on CoRal v3.

Uses. Checkpoints are meant for runtimes that expose variable audio context—e.g. whisper.cpp with --audio-context, or FUTO Keyboard on Android. Under default full-context Whisper settings they behave like ordinary Whisper. Pre-converted GGML models and usage notes: whisper-acft README.

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Overview

train_acft.py  →  small_244M_danish_whisper_acft_futo_best/  →  convert-to-android.sh  →  android-futo-ready/
   (PyTorch)              (HF weights + tokenizer)              (convert)           (ggml-model.bin)

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Prerequisites

• uv and Python ≥ 3.13 (see pyproject.toml)
• CUDA GPU recommended for training
• Git submodules / clones used by conversion:
  • whisper/ — OpenAI reference repo (vocab layout for convert-h5-to-ggml.py)
  • whisper.cpp/ — contains models/convert-h5-to-ggml.py
• Hugging Face token for the CoRal dataset (create .env):

HF_TOKEN=hf_...

Install dependencies:

uv sync

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train_acft.py

FUTO-style ACFT (audio-context fine-tuning) distillation for Danish ASR. The student model is trained to match the teacher’s decoder hidden states when the encoder only sees a variable-length partial mel context (simulating short streaming chunks), while the frozen teacher always uses the full 1500-frame encoder context.

What it does

• Loads openai/whisper-small as both student (model_train) and teacher (model_base).
• Streams CoRal v3 read_aloud at 16 kHz; skips clips longer than 29 s.
• For each sample, picks a random partial encoder length n_ctx (with jitter), runs MSE loss between student and teacher decoder hidden states, then optimizes with AdamW (batch size 1).
• Logs to TensorBoard; saves checkpoints on improved EMA loss every 500 steps; early-stops after 20 evals without improvement or at 20 000 steps.
• On exit (normal, early stop, max steps, or Ctrl+C), always writes a latest checkpoint directory.

Usage

uv run python train_acft.py --size small

Output directories

Training writes Hugging Face–format folders (config, tokenizer, model.safetensors). Naming pattern from the script:

• Best (lowest EMA loss): small_244M_danish_whisper_acft_futo_best
• Latest (always saved at end): small_244M_danish_whisper_acft_futo_latest

Hyperparameters (in script)

Best run (reference)

From training logs for the bundled checkpoint:

[Step 24000] New best loss: 0.0313. Saved checkpoint to small_244M_danish_whisper_acft_futo_best

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small_244M_danish_whisper_acft_futo_best

Hugging Face–format Whisper small checkpoint (244M parameters) after ACFT distillation. Use this folder as input to convert-to-android.sh or load directly with Transformers.

Contents

Load in Python (example)

from transformers import WhisperForConditionalGeneration, WhisperProcessor

model_dir = "small_244M_best_danish_whisper_acft_futo"
model = WhisperForConditionalGeneration.from_pretrained(model_dir)
processor = WhisperProcessor.from_pretrained(model_dir)

Architecture matches OpenAI whisper-small (d_model=768, 12 encoder/decoder layers).

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convert-to-android.sh

Batch-converts trained HF model directories into GGML binaries for whisper.cpp / FUTO Keyboard.

Behavior

1. Discovers folders (default: any directory name containing danish_whisper_acft_futo in the project root).
2. Downloads added_tokens.json and vocab.json from openai/whisper-small into each folder (ensures tokenizer compatibility with the converter).
3. Runs uv run whisper.cpp/models/convert-h5-to-ggml.py <folder>/ ./whisper/ <folder>/
4. Moves ggml-model.bin → android-futo-ready/<folder>.bin

Usage

# Convert all danish_whisper_acft_futo* folders in the current directory
./convert-to-android.sh

# Convert one or more folders explicitly (space-separated)
./convert-to-android.sh small_244M_danish_whisper_acft_futo_best

Make the script executable if needed:

chmod +x convert-to-android.sh

Requirements

• Run from the repository root.
• whisper/ and whisper.cpp/ must be present.
• Each source folder must contain a full HF checkpoint (including model.safetensors or equivalent weights the converter accepts).

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android-futo-ready

Output directory for the GGML model consumed on-device (e.g. FUTO Keyboard Android voice input).

Regenerate after retraining or updating the HF folder:

./convert-to-android.sh small_244M_danish_whisper_acft_futo_best

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End-to-end

# 1. Train (or skip if using the bundled best checkpoint)
uv run python train_acft.py --size small

# 2. Convert best small checkpoint to GGML
./convert-to-android.sh small_244M_danish_whisper_acft_futo_best

# 3. Deploy
# Copy android-futo-ready/small_244M_danish_whisper_acft_futo_best.bin into your Android / FUTO build.

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Troubleshooting