whisper-bambara-asr-002
This model is a fine-tuned version of oza75/whisper-bambara-asr-002 on an Bambara ASR dataset. It achieves the following results on the evaluation set (Evaluation set was very small for training speed purpose):
- Loss: 1.3643
- Wer: 53.2541
Usage
To use this model, we first need to define a Tokenizer class because the default Whisper tokenizer does not support Bambara.
IMPORTANT: The following code will also override the Whisper tokenizer's LANGUAGES constants. This is not the ideal approach, but it is effective. If you do not make this modification, the generation process will fail.
from typing import List
from tokenizers import AddedToken
from transformers import WhisperTokenizer, WhisperProcessor
import transformers.models.whisper.tokenization_whisper as whisper_tokenization
from transformers.models.whisper.tokenization_whisper import TO_LANGUAGE_CODE, TASK_IDS
CUSTOM_TO_LANGUAGE_CODE = {**TO_LANGUAGE_CODE, "bambara": "bm"}
# IMPORTANT: We update the whisper tokenizer constants to add Bambara Language. Not ideal but at least it works
whisper_tokenization.TO_LANGUAGE_CODE.update(CUSTOM_TO_LANGUAGE_CODE)
class BambaraWhisperTokenizer(WhisperTokenizer):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.add_tokens(AddedToken(content="<|bm|>", lstrip=False, rstrip=False, normalized=False, special=True))
@property
def prefix_tokens(self) -> List[int]:
bos_token_id = self.convert_tokens_to_ids("<|startoftranscript|>")
translate_token_id = self.convert_tokens_to_ids("<|translate|>")
transcribe_token_id = self.convert_tokens_to_ids("<|transcribe|>")
notimestamps_token_id = self.convert_tokens_to_ids("<|notimestamps|>")
if self.language is not None:
self.language = self.language.lower()
if self.language in CUSTOM_TO_LANGUAGE_CODE:
language_id = CUSTOM_TO_LANGUAGE_CODE[self.language]
elif self.language in CUSTOM_TO_LANGUAGE_CODE.values():
language_id = self.language
else:
is_language_code = len(self.language) == 2
raise ValueError(
f"Unsupported language: {self.language}. Language should be one of:"
f" {list(CUSTOM_TO_LANGUAGE_CODE.values()) if is_language_code else list(CUSTOM_TO_LANGUAGE_CODE.keys())}."
)
if self.task is not None:
if self.task not in TASK_IDS:
raise ValueError(f"Unsupported task: {self.task}. Task should be in: {TASK_IDS}")
bos_sequence = [bos_token_id]
if self.language is not None:
bos_sequence.append(self.convert_tokens_to_ids(f"<|{language_id}|>"))
if self.task is not None:
bos_sequence.append(transcribe_token_id if self.task == "transcribe" else translate_token_id)
if not self.predict_timestamps:
bos_sequence.append(notimestamps_token_id)
return bos_sequence
Then, we can define the pipeline:
import torch
from transformers import pipeline
# Determine the appropriate device (GPU or CPU)
device = "cuda" if torch.cuda.is_available() else "cpu"
# Define the model checkpoint and language
model_checkpoint = "oza75/whisper-bambara-asr-001"
language = "bambara"
# Load the custom tokenizer designed for Bambara and the ASR model
tokenizer = BambaraWhisperTokenizer.from_pretrained(model_checkpoint, language=language, device=device)
pipe = pipeline(model=model_checkpoint, tokenizer=tokenizer, device=device)
def transcribe(audio):
"""
Transcribes the provided audio file into text using the configured ASR pipeline.
Args:
audio: The path to the audio file to transcribe.
Returns:
A string representing the transcribed text.
"""
# Use the pipeline to perform transcription
text = pipe(audio)["text"]
return text
transcribe(path_to_the_audio)
Intended uses & limitations
This checkpoint is intended to be used ONLY for research purposes !!!
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 4
- total_train_batch_size: 192
- total_eval_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
1.2108 | 0.7752 | 100 | 1.1191 | 77.3885 |
0.848 | 1.5504 | 200 | 0.8848 | 64.0640 |
0.694 | 2.3256 | 300 | 0.8022 | 62.0272 |
0.6062 | 3.1008 | 400 | 0.7607 | 67.0102 |
0.5617 | 3.8760 | 500 | 0.7314 | 59.4083 |
0.4565 | 4.6512 | 600 | 0.7334 | 69.0713 |
0.3455 | 5.4264 | 700 | 0.7656 | 59.8812 |
0.2621 | 6.2016 | 800 | 0.8062 | 68.1256 |
0.2672 | 6.9767 | 900 | 0.8130 | 56.9593 |
0.1916 | 7.7519 | 1000 | 0.8706 | 58.0868 |
0.1302 | 8.5271 | 1100 | 0.9390 | 58.2565 |
0.0785 | 9.3023 | 1200 | 0.9932 | 55.5286 |
0.0785 | 0.7752 | 1300 | 1.0391 | 55.8802 |
0.0495 | 1.5504 | 1400 | 1.0820 | 58.4627 |
0.032 | 2.3256 | 1500 | 1.1270 | 55.2498 |
0.026 | 3.1008 | 1600 | 1.1660 | 57.4321 |
0.0241 | 3.8760 | 1700 | 1.1738 | 53.5766 |
0.019 | 4.6512 | 1800 | 1.1943 | 53.6736 |
0.0149 | 5.4264 | 1900 | 1.2236 | 52.3642 |
0.0116 | 6.2016 | 2000 | 1.2549 | 58.8143 |
0.014 | 6.9767 | 2100 | 1.25 | 52.1581 |
0.0121 | 7.7519 | 2200 | 1.2627 | 51.3094 |
0.0106 | 8.5271 | 2300 | 1.2705 | 52.6673 |
0.0097 | 9.3023 | 2400 | 1.2744 | 53.0674 |
0.0079 | 0.7797 | 2500 | 1.2803 | 58.1741 |
0.0071 | 1.5595 | 2600 | 1.2979 | 55.2040 |
0.0058 | 2.3392 | 2700 | 1.3174 | 54.9199 |
0.0052 | 3.1189 | 2800 | 1.3281 | 56.4954 |
0.0056 | 3.8986 | 2900 | 1.3193 | 51.3946 |
0.0051 | 4.6784 | 3000 | 1.3291 | 49.9483 |
0.0045 | 5.4581 | 3100 | 1.3428 | 52.4019 |
0.0036 | 6.2378 | 3200 | 1.3506 | 49.0186 |
0.0041 | 7.0175 | 3300 | 1.3623 | 50.2583 |
0.0047 | 7.7973 | 3400 | 1.3643 | 53.2541 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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