mahwizzzz/whisper-urdu-pytorch

Urdu Whisper model in Pytorch from scratch implementation

Trained a small Urdu whisper model

Robust Speech Recognition via Large-Scale Weak Supervision

ModelArgs Hyperparameters

Parameter	Value	Description
batch_size	128	The number of samples processed before the model is updated.
max_lr	1.5e-3	Maximum learning rate.
dropout	0.1	Dropout rate for regularization.
epochs	2	Number of training epochs.
block_size	64	Sequence length (number of tokens or time steps).
tgt_vocab_size	200024	Size of the target vocabulary.
embeddings_dims	512	Dimensionality of token embeddings.
attn_dropout	0.1	Dropout rate for attention layers.
no_of_heads	4	Number of attention heads in multi-head attention.
no_of_decoder_layers	6	Number of decoder layers in the model.
weight_decay_optim	0.1	Weight decay for the optimizer.
log_mel_features	80	Number of Mel spectrogram features.
kernel_size	3	Kernel size for convolutional layers.
stride	2	Stride for convolutional layers.
sr	16000	Sampling rate of the audio.
device	'cuda:0'	Device to run the model on (e.g., GPU).
SAMPLING_RATE	16000	Sampling rate of the audio.
N_MELS	80	Number of Mel bins in the spectrogram.
WINDOW_DURATION	0.025	Duration of the analysis window in seconds (25 ms).
STRIDE_DURATION	0.010	Stride between consecutive windows in seconds (10 ms).
max_t	500	Maximum time steps in the spectrogram.
n_channels	80	Number of channels in the input spectrogram.

Dataset

Common Voice Corpus 11.0

Used the 'xs' snapshot.

Frameworks:

Pytorch

Epochs/Steps

Epochs (train) = 2

Val iterations = every epoch

Loss Curves