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Aynkader/
Balochi_STT

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Balochi STT (Whisper)

Fine-tuned Whisper-small speech-to-text model for Balochi (Latin script).

Item	Value
Task	Automatic speech recognition (ASR / STT)
Language	Balochi (Latin orthography)
Base model	Whisper-small
Sample rate	16 kHz, mono
Best eval WER	~4.5%
Training data	5,237 clips (~3 hours) from `wavs/wavs` + `wavs/wavs.txt`

Folder layout

text

balochi_whisper/
├── README.md
├── best_model/
│   ├── model/          # Whisper weights + config
│   ├── processor/      # tokenizer + feature extractor
│   └── eval_metrics.txt
└── checkpoints/        # training checkpoints (optional)

Use best_model/ for inference.

Requirements

source ~/venvs/torch/bin/activate

Needs: torch, transformers, soundfile, torchaudio, numpy.

GPU is recommended (tested on NVIDIA RTX 3060).

Quick inference (Python)

python

from pathlib import Path
import numpy as np
import soundfile as sf
import torch
import torchaudio
from transformers import WhisperForConditionalGeneration, WhisperProcessor

ROOT = Path("balochi_whisper/best_model")
device = "cuda" if torch.cuda.is_available() else "cpu"

processor = WhisperProcessor.from_pretrained(ROOT / "processor")
model = WhisperForConditionalGeneration.from_pretrained(ROOT / "model").to(device)
model.eval()
model.config.forced_decoder_ids = None
model.generation_config.forced_decoder_ids = None
model.generation_config.pad_token_id = processor.tokenizer.pad_token_id

def load_16k(path: str) -> np.ndarray:
    wav, sr = sf.read(path, always_2d=False)
    if wav.ndim > 1:
        wav = wav.mean(axis=1)
    wav = np.asarray(wav, dtype=np.float32)
    if sr != 16000:
        wav = (
            torchaudio.functional.resample(
                torch.from_numpy(wav).unsqueeze(0), sr, 16000
            )
            .squeeze(0)
            .numpy()
        )
    return wav

wav = load_16k("your_audio.wav")
inputs = processor(
    wav, sampling_rate=16000, return_tensors="pt", return_attention_mask=True
)
with torch.no_grad():
    ids = model.generate(
        inputs.input_features.to(device),
        attention_mask=inputs.attention_mask.to(device),
        max_new_tokens=224,
    )
text = processor.batch_decode(ids.cpu(), skip_special_tokens=True)[0].strip()
print(text)

CLI (from project root)

bash

cd ~/Balochi_tts
source ~/venvs/torch/bin/activate
python infer_stt.py /path/to/audio.wav

Desktop GUI:

python stt_gui.py

Training data format

text

wavs/wavs/1.wav … N.wav
wavs/wavs.txt          # line i → transcript for (i+1).wav

Transcripts use Balochi Latin script (á, é, ó allowed).

Retrain / resume from project root:

python train_stt.py

Checkpoints are saved under balochi_whisper/checkpoints/. The best model (lowest WER) is copied to balochi_whisper/best_model/.

Hugging Face

Upload this model:

bash

# from Balochi_tts project root
bash scripts/push_models_to_hf.sh YOUR_HF_USER

That publishes YOUR_HF_USER/balochi-whisper-stt with model/ and processor/.

Load from the Hub:

python

from transformers import WhisperForConditionalGeneration, WhisperProcessor

repo = "YOUR_HF_USER/balochi-whisper-stt"
processor = WhisperProcessor.from_pretrained(repo, subfolder="processor")
model = WhisperForConditionalGeneration.from_pretrained(repo, subfolder="model")

If you upload files at the repo root (no subfolders), omit subfolder=....

Notes

Input audio is converted to 16 kHz mono before recognition.
Audio longer than 30 seconds should be chunked (the project CLI/GUI do this).
Output is Balochi Latin text, not Arabic script.
Do not force an English language token; Balochi is not a built-in Whisper language.

Files to ship

Minimum files for inference:

text

best_model/model/config.json
best_model/model/generation_config.json
best_model/model/model.safetensors
best_model/processor/   # full processor directory

checkpoints/ is only needed to resume training.

Aynkader/Balochi_STT

Balochi STT (Whisper)

Folder layout

Requirements

Quick inference (Python)

CLI (from project root)

Training data format

Hugging Face

Notes

Files to ship

Aynkader/
Balochi_STT