wav2vec2-large-xls-r-300m-Urdu

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It achieves the following results on the evaluation set:

• Loss: 0.9889
• Wer: 0.5607
• Cer: 0.2370

Evaluation Commands

1. To evaluate on mozilla-foundation/common_voice_8_0 with split test

python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-300m-Urdu --dataset mozilla-foundation/common_voice_8_0 --config ur --split test

Inference With LM

# pip install transformers datasets pyctcdecode kenlm huggingface_hub torch

import json, torch
from datasets import load_dataset, Audio
from transformers import AutoProcessor, AutoModelForCTC
from pyctcdecode import build_ctcdecoder
from huggingface_hub import hf_hub_download

mid = "kingabzpro/wav2vec2-large-xls-r-300m-Urdu"
proc = AutoProcessor.from_pretrained(mid)
model = AutoModelForCTC.from_pretrained(mid).eval().to(
    "cuda" if torch.cuda.is_available() else "cpu"
)

kenlm = hf_hub_download(mid, "language_model/5gram.bin")
uni  = hf_hub_download(mid, "language_model/unigrams.txt")
try: attrs = json.load(open(hf_hub_download(mid, "language_model/attrs.json"), encoding="utf-8"))
except: attrs = {}

v = proc.tokenizer.get_vocab()
id2tok = [t for t,i in sorted(v.items(), key=lambda x:x[1])]
blank = proc.tokenizer.pad_token_id; wdt = proc.tokenizer.word_delimiter_token
keep, labels = zip(*[
    (i, "" if i==blank else " " if t==wdt else t)
    for i,t in enumerate(id2tok) if (i==blank or t==wdt or len(t)==1)
])

dec = build_ctcdecoder(list(labels), kenlm_model_path=kenlm,
                       unigrams=open(uni,encoding="utf-8").read().splitlines())
dec.alpha, dec.beta = attrs.get("alpha",0.5), attrs.get("beta",1.0)

ds = load_dataset("mozilla-foundation/common_voice_22_0", "ur", split="test", streaming=True)
ex = next(iter(ds.cast_column("audio", Audio(sampling_rate=16_000))))
x = proc(ex["audio"]["array"], sampling_rate=16_000, return_tensors="pt").input_values.to(model.device)

with torch.no_grad():
    logits = model(x).logits[0].cpu().numpy()[:, keep]
print(dec.decode(logits))

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 200

Training results

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.2.dev0
• Tokenizers 0.11.0

Eval results on Common Voice 8 "test" (WER):