inference.py

#!/usr/bin/env python

# pip install transformers datasets torch soundfile jiwer

from datasets import load_dataset, Audio
from transformers import pipeline, WhisperProcessor
from torch.utils.data import DataLoader
import torch
from jiwer import wer as jiwer_wer
from jiwer import cer as jiwer_cer
import ipdb

# 1. Load FLEURS Burmese test set, cast to 16 kHz audio
ds = load_dataset("google/fleurs", "km_kh", split="test")
ds = ds.cast_column("audio", Audio(sampling_rate=16_000))

from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline


device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

# model_id = "openai/whisper-large-v3"
model_id = "pengyizhou/whisper-fleurs-km_kh"

model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
whisper_model = "openai/whisper-large-v3"
processor = WhisperProcessor.from_pretrained(whisper_model, language="khmer")

asr = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    torch_dtype=torch_dtype,
    chunk_length_s=30,
    batch_size=64,
    max_new_tokens=440,
    device=device,
    no_repeat_ngram_size=3,        # Prevent repeating 3-grams
    repetition_penalty=1.0,        # Penalize repetitions (>1.0 reduces repetition)
    length_penalty=1.0,            # Control length preference
    num_beams=1,                   # Use beam search for better quality
    do_sample=False,               # Disable sampling for deterministic output
    early_stopping=False,           # Stop when sufficient beams are complete
    suppress_tokens=[],
)


# 3. Batch‐wise transcription function
def transcribe_batch(batch):
    # `batch["audio"]` is a list of {"array": np.ndarray, ...}
    inputs = [ ex["array"] for ex in batch["audio"] ]
    outputs = asr(inputs)  # returns a list of dicts with "text"
    # lower-case and strip to normalize for CER
    preds = [ out["text"].lower().strip() for out in outputs ]
    return {"prediction": preds}

# 4. Map over the dataset in chunks of, say, 32 examples at a time
result = ds.map(
    transcribe_batch,
    batched=True,
    batch_size=64,              # feed 32 audios → pipeline will sub-batch into 8s
    remove_columns=ds.column_names
)

# ipdb.set_trace()
# 5. Compute corpus-level CER with jiwer
# refs = "\n".join(t.lower().strip() for t in ds["transcription"])
# preds = "\n".join(t for t in result["prediction"])
# score = jiwer_cer(refs, preds)
refs = [t.lower().strip() for t in ds["transcription"]]
preds = [t for t in result["prediction"]]
score_cer = jiwer_cer(refs, preds)
score_wer = jiwer_wer(refs, preds)

print(f"CER on FLEURS km_kh: {score_cer*100:.2f}%")
print(f"WER on FLEURS km_kh: {score_wer*100:.2f}%")

with open("./km_kh_finetune.pred", "w") as pred_results:
    for pred in preds:
        pred_results.write("{}\n".format(pred))

with open("./km_kh.ref", "w") as ref_results:
    for ref in refs:
        ref_results.write("{}\n".format(ref))