templates/model_card.md

{{model_name}}

{{model_description}}

Usage

import torch
from transformers import AutoProcessor, AutoModelForSeq2SeqLM
import soundfile as sf

processor = AutoProcessor.from_pretrained("{{repo_name}}")
model = AutoModelForSeq2SeqLM.from_pretrained(
    "{{repo_name}}",
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
)

audio, sr = sf.read("sample.wav")
inputs = processor(audio, sampling_rate=sr, return_tensors="pt")
with torch.no_grad():
    generated_ids = model.generate(**inputs, max_new_tokens=256)
text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(text)

Training Configuration

• Base model: {{base_model}} {{#if training_config_type}}- Config: {{training_config_type}}{{/if}}

{{#if trainer_type}}- Trainer: {{trainer_type}}{{/if}}

Training Parameters

• Batch size: {{batch_size}}
• Grad accumulation: {{gradient_accumulation_steps}}
• Learning rate: {{learning_rate}}
• Max epochs: {{max_epochs}}
• Sequence length: {{max_seq_length}}

Hardware

• {{hardware_info}}

Notes

• This repository contains a fine-tuned Voxtral ASR model.