whisper-medium-mn

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Whisper Medium Mn - Erkhembayar Gantulga

This model is a fine-tuned version of openai/whisper-medium on the Common Voice 17.0 and Google Fleurs datasets. It achieves the following results on the evaluation set:

• Loss: 0.1083
• Wer: 12.9580

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

Datasets used for training:

• Common Voice 17.0
• Google Fleurs

For training, combined Common Voice 17.0 and Google Fleurs datasets:

from datasets import load_dataset, DatasetDict, concatenate_datasets
from datasets import Audio

common_voice = DatasetDict()

common_voice["train"] = load_dataset("mozilla-foundation/common_voice_17_0", "mn", split="train+validation+validated", use_auth_token=True)
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_17_0", "mn", split="test", use_auth_token=True)

common_voice = common_voice.cast_column("audio", Audio(sampling_rate=16000))

common_voice = common_voice.remove_columns(
    ["accent", "age", "client_id", "down_votes", "gender", "locale", "path", "segment", "up_votes", "variant"]
)

google_fleurs = DatasetDict()

google_fleurs["train"] = load_dataset("google/fleurs", "mn_mn", split="train+validation", use_auth_token=True)
google_fleurs["test"] = load_dataset("google/fleurs", "mn_mn", split="test", use_auth_token=True)

google_fleurs = google_fleurs.remove_columns(
    ["id", "num_samples", "path", "raw_transcription", "gender", "lang_id", "language", "lang_group_id"]
)
google_fleurs = google_fleurs.rename_column("transcription", "sentence")

dataset = DatasetDict()
dataset["train"] = concatenate_datasets([common_voice["train"], google_fleurs["train"]])
dataset["test"] = concatenate_datasets([common_voice["test"], google_fleurs["test"]])

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• training_steps: 4000
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.44.0
• Pytorch 2.3.1+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1