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#!/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 Cebuano test set, cast to 16 kHz audio
ds = load_dataset("google/fleurs", "ceb_ph", 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 = 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")
processor = WhisperProcessor.from_pretrained(whisper_model, language="tagalog")
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,
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, generate_kwargs={"language": "khmer"}) # 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 ceb_ph: {score_cer*100:.2f}%")
print(f"WER on FLEURS ceb_ph: {score_wer*100:.2f}%")
with open("./ceb_ph_zeroshot.pred", "w") as pred_results:
for pred in preds:
pred_results.write("{}\n".format(pred))
with open("./ceb_ph.ref", "w") as ref_results:
for ref in refs:
ref_results.write("{}\n".format(ref))
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