README.md

metadata

language:
  - ga
  - en
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
  - generated_from_trainer
datasets:
  - ymoslem/IWSLT2023-GA-EN
  - ymoslem/FLEURS-GA-EN
  - ymoslem/BitesizeIrish-GA-EN
  - ymoslem/SpokenWords-GA-EN-MTed
  - ymoslem/Tatoeba-Speech-Irish
  - ymoslem/Wikimedia-Speech-Irish
  - ymoslem/EUbookshop-Speech-Irish
metrics:
  - bleu
  - wer
model-index:
  - name: Whisper Larget V3 GA-EN Speech Translation
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: >-
            IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and
            EUbookshop
          type: ymoslem/IWSLT2023-GA-EN
        metrics:
          - name: Bleu
            type: bleu
            value: 15.23
          - name: Wer
            type: wer
            value: 92.70598829356146

Whisper Larget V3 GA-EN Speech Translation

This model is a fine-tuned version of openai/whisper-large-v3 on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop dataset. It achieves the following results on the evaluation set:

• Loss: 0.9885
• Bleu: 15.23
• Chrf: 28.15
• Wer: 92.7060

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.03
• training_steps: 4000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Bleu	Chrf	Validation Loss	Wer
2.5918	0.0138	100	0.61	8.48	2.1791	238.2260
2.476	0.0276	200	0.63	10.43	2.1702	275.7317
2.2358	0.0414	300	4.76	19.98	2.0420	120.0810
2.1778	0.0552	400	2.78	12.85	1.9506	86.8528
1.9779	0.0690	500	4.53	18.47	1.8609	137.1905
1.9435	0.0828	600	6.67	22.37	1.7726	82.4403
1.7928	0.0966	700	4.54	17.32	1.7445	133.8586
1.9004	0.1103	800	1.58	12.65	1.7290	195.2724
1.7856	0.1241	900	4.84	17.5	1.6990	83.9262
1.6783	0.1379	1000	8.46	24.24	1.6329	113.5074
1.6095	0.1517	1100	7.35	20.22	1.6083	102.5214
1.6328	0.1655	1200	11.46	25.29	1.5267	76.5871
1.6093	0.1793	1300	6.51	17.77	1.4947	112.4719
1.5776	0.1931	1400	6.21	19.86	1.4952	90.6348
1.4767	0.2069	1500	4.86	19.57	1.4515	145.1148
1.3447	0.2207	1600	6.77	19.96	1.3974	90.5448
1.3273	0.2345	1700	4.77	16.31	1.4323	152.1837
1.4253	0.2483	1800	3.95	15.66	1.3598	173.2553
1.3505	0.2621	1900	11.25	23.4	1.3517	80.3692
1.2593	0.2759	2000	12.71	26.55	1.3236	77.5777
1.2483	0.2897	2100	17.88	32.0	1.2825	73.3003
1.161	0.3034	2200	10.08	20.69	1.2567	115.8937
1.1597	0.3172	2300	8.61	19.54	1.2581	93.8766
1.0937	0.3310	2400	12.37	25.67	1.2577	99.0095
1.0606	0.3448	2500	6.46	23.47	1.2228	172.9401
1.039	0.3586	2600	9.55	21.56	1.2186	89.7794
1.0193	0.3724	2700	3.08	17.58	1.1844	281.8100
1.1153	0.3862	2800	1.1693	2.69	18.38	350.2927
1.012	0.4	2900	1.1233	3.56	14.74	194.9122
0.8936	0.4138	3000	1.1161	5.21	17.38	158.3521
0.8893	0.4276	3100	1.1119	11.52	25.02	80.9095
0.9491	0.4414	3200	1.1213	5.93	20.91	174.0207
0.9233	0.4552	3300	1.0656	5.54	20.95	186.2224
0.8915	0.4690	3400	1.0736	7.26	23.99	155.6506
0.8296	0.4828	3500	1.0461	6.74	21.46	146.1054
0.8163	0.4966	3600	1.0706	11.35	24.11	101.8010
0.8115	0.5103	3700	1.0199	12.84	26.92	115.8487
0.8245	0.5241	3800	1.0163	12.47	24.29	101.9361
0.7988	0.5379	3900	0.9891	15.29	28.54	92.7960
0.769	0.5517	4000	0.9885	15.23	28.15	92.7060

Framework versions

• Transformers 4.41.2
• Pytorch 2.1.2+git70dfd51
• Datasets 2.20.0
• Tokenizers 0.19.1