Model Card for Model ID
[SUMMARY HERE]
Model Details
Model Description
- Developed by: Jesse Arzate
- Model type: Sequence-to-Sequence (Seq2Seq) Transformer-based model
- Language(s) (NLP): English
- License: [More Information Needed]
- Finetuned from model [optional]: Whisper ASR: distil-large-v3
Model Sources [optional]
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import (
AutomaticSpeechRecognitionPipeline,
WhisperForConditionalGeneration,
WhisperTokenizer,
WhisperProcessor,
)
from peft import PeftModel, PeftConfig
peft_model_id = "baileyarzate/whisper-distil-large-v3-atc-english" # huggingface model path
language = "en"
task = "transcribe"
device = 'cuda'
peft_config = PeftConfig.from_pretrained(peft_model_id)
model = WhisperForConditionalGeneration.from_pretrained(
peft_config.base_model_name_or_path, device_map="cuda"
).to(device)
model = PeftModel.from_pretrained(model, peft_model_id).to(device)
tokenizer = WhisperTokenizer.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)
processor = WhisperProcessor.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)
feature_extractor = processor.feature_extractor
forced_decoder_ids = processor.get_decoder_prompt_ids(language=language, task=task)
pipe = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)
model.config.use_cache = True
def transcribe(audio):
with torch.cuda.amp.autocast():
text = pipe(audio, generate_kwargs={"forced_decoder_ids": forced_decoder_ids}, max_new_tokens=255)["text"]
return text
transcriptions_finetuned = []
for i in tqdm(range(len(df_subset))):
# When you only have audio file path
#transcriptions_finetuned.append(transcribe(librosa.load(df["path"][i], sr = 16000, offset = df["start"][i], duration = df["stop"][i] - df["start"][i])[0])) #,model
# When you have audio array, saves time
transcriptions_finetuned.append(transcribe(df_subset['array'].iloc[i]))
transcriptions_finetuned = pd.DataFrame(transcriptions_finetuned, columns=['transcription_finetuned'])
df_subset = df_subset.reset_index().drop(columns=['index'])
df_subset = pd.concat([df_subset, transcriptions_finetuned], axis=1)
Training Details
Training Data
Dataset: ATC audio recordings from actual flight operations. Size: ~250 hours of annotated data.
Training Procedure
Modeled the procedure after: https://github.com/Vaibhavs10/fast-whisper-finetuning
Preprocessing [optional]
Preprocessing: Striped leading and trailing whitespaces from transcript sentences. Removed any sentences containing the phrase "UNINTELLIGIBLE" to filter out unclear or garbled speech. Removed filler words such as "ah" or "uh".
Training Hyperparameters
- Training regime: [More Information Needed]
training_args = Seq2SeqTrainingArguments(
per_device_train_batch_size=4,
gradient_accumulation_steps=2,
learning_rate=5e-4,
warmup_steps=100,
num_train_epochs=3,
fp16=True,
per_device_eval_batch_size=4,
generation_max_length=128,
logging_steps=100,
save_steps=500,
save_total_limit=3,
remove_unused_columns=False, # required as the PeftModel forward doesn't have the signature of the wrapped model's forward
label_names=["labels"], # same reason as above
)
Speeds, Sizes, Times [optional]
Inference time is about 2 samples per second with an RTX A2000.
Evaluation
Final training loss: 0.103
Testing Data, Factors & Metrics
Testing Data
Dataset: ATC audio recordings from actual flight operations. Size: ~250 hours of annotated data. Randomly sampled 20% of the data with seed = 42.
[More Information Needed]
Factors
[More Information Needed]
Metrics
Word Error Rate, Normalized Word Error Rate
Results
Mean WER for 500 test samples: 0.145 with 95% confidence interval: (0.123, 0.167)
Summary
[IN PROGRESS]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: RTX A2000
- Hours used: 24
- Cloud Provider: Private Infrustructure
- Compute Region: Southern California
- Carbon Emitted: 1.57 kg
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
- CPU: AMD EPYC 7313P 16-Core Processor 3.00 GHz
- GPU: NVIDIA RTX A2000
- vRAM: 6GB
- RAM: 128GB
Software
- OS: Windows 11 Enterprise - 21H2
- Python: Python 3.10.14
Citation [optional]
[IN PROGRESS]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Model Card Contact
Jesse Arzate: baileyarzate@gmail.com