frankmorales2020/Voxtral-Mini-4B-H2E-FineTune

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Article in medium: https://medium.com/ai-simplified-in-plain-english/the-unified-brain-how-voxtral-accelerates-the-agentic-ai-revolution-15a8edced1f2

Github: https://github.com/frank-morales2020/MLxDL/blob/main/FT_V2TXT_DEMO.ipynb

Model Details

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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Uses

Direct Use

import torch
import librosa
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq

# 1. Configuration - Pointing to your new HF repository
REPO_ID = "frankmorales2020/Voxtral-Mini-4B-H2E-FineTune"
AUDIO_PATH = "barackobama2004dncARXE.mp3" # Or any test file

# 2. Load Model and Processor directly from Hugging Face
print(f"📡 Loading model from HF: {REPO_ID}...")
processor = AutoProcessor.from_pretrained(REPO_ID, trust_remote_code=True)
model = AutoModelForSpeechSeq2Seq.from_pretrained(
    REPO_ID,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)
model.eval()

# 3. Audio Preprocessing
speech, sr = librosa.load(AUDIO_PATH, sr=16000)

# 4. Transcription Logic
print("🎙️ Transcribing...")
# Voxtral recommends temperature 0.0 for best accuracy
inputs = processor(audio=speech[:16000*20], sampling_rate=16000, return_tensors="pt")
input_features = inputs.input_features.to(model.device, dtype=torch.bfloat16)

with torch.no_grad():
    generated_ids = model.generate(
        input_features=input_features,
        max_new_tokens=1024,
        do_sample=False,  # Equivalent to temperature 0.0
        use_cache=True
    )

transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(f"\n✅ Result: {transcription.strip()}")

✅ Result: Thank you so much. Thank you so much. Thank you. Thank you. Thank you, Dick Durbin. You make us all proud. On behalf of the great state of Illinois.

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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