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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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Direct Use
1
2import torch
3import librosa
4from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
5
6# 1. Configuration - Pointing to your new HF repository
7REPO_ID = "frankmorales2020/Voxtral-Mini-4B-H2E-FineTune"
8AUDIO_PATH = "barackobama2004dncARXE.mp3" # Or any test file
9
10# 2. Load Model and Processor directly from Hugging Face
11print(f"📡 Loading model from HF: {REPO_ID}...")
12processor = AutoProcessor.from_pretrained(REPO_ID, trust_remote_code=True)
13model = AutoModelForSpeechSeq2Seq.from_pretrained(
14 REPO_ID,
15 torch_dtype=torch.bfloat16,
16 device_map="auto",
17 trust_remote_code=True
18)
19model.eval()
20
21# 3. Audio Preprocessing
22speech, sr = librosa.load(AUDIO_PATH, sr=16000)
23
24# 4. Transcription Logic
25print("🎙️ Transcribing...")
26# Voxtral recommends temperature 0.0 for best accuracy
27inputs = processor(audio=speech[:16000*20], sampling_rate=16000, return_tensors="pt")
28input_features = inputs.input_features.to(model.device, dtype=torch.bfloat16)
29
30with torch.no_grad():
31 generated_ids = model.generate(
32 input_features=input_features,
33 max_new_tokens=1024,
34 do_sample=False, # Equivalent to temperature 0.0
35 use_cache=True
36 )
37
38transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
39print(f"\n✅ Result: {transcription.strip()}")
40
41
1
2✅ 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.
3
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