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erkhem-gantulga
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whisper-medium-mn

Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Mongolian
whisper
audio
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whisper-medium-mn
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---
language:
- mn
base_model: openai/whisper-medium
library_name: transformers
datasets:
- mozilla-foundation/common_voice_17_0
- google/fleurs
tags:
- audio
- automatic-speech-recognition
widget:
- example_title: Common Voice sample 1
  src: sample1.flac
- example_title: Common Voice sample 2
  src: sample2.flac
model-index:
- name: whisper-medium-mn
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 17.0
      type: common_voice_17_0
      config: mn
      split: test
      args: 
        language: mn
    metrics:
    - name: Test WER
      type: wer
      value: 12.9580
pipeline_tag: automatic-speech-recognition
license: apache-2.0
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Whisper Medium Mn - Erkhembayar Gantulga

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/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](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0)
- [Google Fleurs](https://huggingface.co/datasets/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

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2986        | 0.4912 | 500  | 0.3557          | 40.1515 |
| 0.2012        | 0.9823 | 1000 | 0.2310          | 28.3512 |
| 0.099         | 1.4735 | 1500 | 0.1864          | 23.4453 |
| 0.0733        | 1.9646 | 2000 | 0.1405          | 18.3024 |
| 0.0231        | 2.4558 | 2500 | 0.1308          | 16.5645 |
| 0.0191        | 2.9470 | 3000 | 0.1155          | 14.5569 |
| 0.0059        | 3.4381 | 3500 | 0.1122          | 13.4728 |
| 0.006         | 3.9293 | 4000 | 0.1083          | 12.9580 |


### Framework versions

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