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metadata
dataset_info:
  features:
    - name: file_name
      dtype: string
    - name: url
      dtype: string
    - name: inference_transcript
      dtype: string
    - name: audio_duration
      dtype: float64
    - name: original_id
      dtype: string
    - name: strata
      dtype: string
    - name: age_group
      dtype: string
    - name: duration_category
      dtype: string
    - name: content_type
      dtype: string
    - name: path
      dtype: string
    - name: inference_checkpoint-10000
      dtype: string
    - name: inference_checkpoint-19000
      dtype: string
    - name: inference_checkpoint-5000
      dtype: string
    - name: uni
      dtype: string
    - name: base_cer
      dtype: float64
    - name: cer_5000
      dtype: float64
    - name: cer_10000
      dtype: float64
    - name: cer_19000
      dtype: float64
  splits:
    - name: train
      num_bytes: 1205643
      num_examples: 893
  download_size: 393078
  dataset_size: 1205643
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Tibetan Speech Recognition Model Performance Report

Overview

This report summarizes the performance evaluation of a Wav2Vec2-based speech recognition model trained on Tibetan speech data of Garchen Rinpoche. The evaluation was conducted across different training checkpoints using multiple error metrics.

Training datasets

train

Benchmark Dataset Information

  • Total audio duration: 1.07 hours
  • Number of audio segments: 893
  • Average segment duration: 4.31 seconds
  • Content type: Teaching material
  • Speaker age group: 70-90 years

Model Architecture

  • Base Model: ganga4364/mms_300_v4.96000
  • Model Type: Wav2Vec2ForCTC
  • Fine-tuning Method: Parameter-Efficient Fine-Tuning (PEFT) with LoRA

Training Parameters

  • Batch Size: 8 (per device)
  • Gradient Accumulation Steps: 2
  • Learning Rate: 3e-4
  • Training Epochs: 100
  • Warmup Steps: 500
  • FP16 Training: Enabled
  • Evaluation Strategy: Steps-based
    • Logging Interval: 100 steps
    • Evaluation Interval: 1000 steps
    • Save Interval: 1000 steps
  • Save Total Limit: 50 checkpoints
  • Data Loading: 4 workers
  • Monitoring: Weights & Biases (wandb)

Training Progress

Character Error Rate (CER) across training checkpoints:

Checkpoint Micro CER (%)
Base model 27.67
5000 steps 27.41
10000 steps 23.37
19000 steps 22.93

Final Model Performance (Checkpoint 19000)

Word Error Rate (WER)

  • Micro-average WER: 39.42%
  • Macro-average WER: 45.92%

Error Analysis

  • Total test sentences: 893
  • Error breakdown:
    • Substitutions: 4,217
    • Insertions: 779
    • Deletions: 1,190

Sample Prediction

Example transcription:

Reference:
དེའི་རྩིས་གཞི་ད་ད་ལྟ་སྐད་ཆ་བཤད་དགོས་རེད་ད། དྲན་པས་ཡང་མི་གནོད།

Model Prediction:
དེ་རིང་དེ་དུ་དག་ད་ྟ་སྐད་ཆ་བཤད་དགོས་རེད། ད་དྲིརིང་གི་ཡང་མི་འདུག

CER for this example: 39.34%

Key Findings

  1. Consistent Improvement: The model shows steady improvement in CER across training checkpoints, with a total reduction of 4.74 percentage points from base model to final checkpoint.

  2. Character vs Word Accuracy: While the final character-level error rate is relatively low (19.46%), the word-level error rate is higher (39.42%), indicating challenges in maintaining word integrity during recognition.

  3. Error Distribution: The majority of errors are substitutions (4,217), followed by deletions (1,190) and insertions (779), suggesting the model is more prone to replacing characters than inserting or deleting them.

Conclusion

The model demonstrates promising performance for Tibetan speech recognition, particularly at the character level. However, the higher word error rate suggests room for improvement in capturing complete word structures. Future work might focus on reducing the gap between character and word-level accuracy.

Report generated on July 14, 2025

Dataset Statistics

Configuration: default

Split: train

Total Rows: 893

audio_duration

  • Type: numerical
  • Data Type: float64
  • Sum: 3,870.19
  • Average: 4.33

strata

  • Type: categorical
  • Data Type: object
  • Unique Values: 8

Value Distribution:

Value Count Percentage
70-80__medium__Prayer 125 14.00%
70-80__short__Teaching 123 13.77%
70-80__long__Teaching 123 13.77%
70-80__long__Prayer 121 13.55%
70-80__medium__Teaching 118 13.21%
70-80__long__Q&A 108 12.09%
80-90__long__Practice 108 12.09%
70-80__medium__Q&A 67 7.50%

age_group

  • Type: categorical
  • Data Type: object
  • Unique Values: 2

Value Distribution:

Value Count Percentage
70-80 785 87.91%
80-90 108 12.09%

duration_category

  • Type: categorical
  • Data Type: object
  • Unique Values: 3

Value Distribution:

Value Count Percentage
long 460 51.51%
medium 310 34.71%
short 123 13.77%

content_type

  • Type: categorical
  • Data Type: object
  • Unique Values: 4

Value Distribution:

Value Count Percentage
Teaching 364 40.76%
Prayer 246 27.55%
Q&A 175 19.60%
Practice 108 12.09%

base_cer

  • Type: numerical
  • Data Type: float64
  • Sum: 247.09
  • Average: 0.28

cer_5000

  • Type: numerical
  • Data Type: float64
  • Sum: 244.76
  • Average: 0.27

cer_10000

  • Type: numerical
  • Data Type: float64
  • Sum: 208.71
  • Average: 0.23

cer_19000

  • Type: numerical
  • Data Type: float64
  • Sum: 204.79
  • Average: 0.23