🚀 Voxtral Mini 3B - 2507 (Transformers Edition)
Voxtral Mini enhances Ministral 3B by integrating cutting - edge audio input capabilities while maintaining top - notch text performance. It shines in speech transcription, translation, and audio understanding.
Learn more about Voxtral in our blog post here.
✨ Features
Voxtral builds upon Ministral - 3B with powerful audio understanding capabilities:
- Dedicated transcription mode: Voxtral can operate in a pure speech transcription mode to maximize performance. By default, it automatically predicts the source audio language and transcribes the text accordingly.
- Long - form context: With a 32k token context length, Voxtral can handle audios up to 30 minutes for transcription or 40 minutes for understanding.
- Built - in Q&A and summarization: Supports asking questions directly through audio. It can analyze audio and generate structured summaries without the need for separate ASR and language models.
- Natively multilingual: Automatic language detection and state - of - the - art performance in the world’s most widely used languages (English, Spanish, French, Portuguese, Hindi, German, Dutch, Italian).
- Function - calling straight from voice: Enables direct triggering of backend functions, workflows, or API calls based on spoken user intents.
- Highly capable at text: Retains the text understanding capabilities of its language model backbone, Ministral - 3B.
📈 Benchmark Results
Audio
Average word error rate (WER) over the FLEURS, Mozilla Common Voice and Multilingual LibriSpeech benchmarks:
Text
📦 Installation
Install Transformers from source:
pip install git+https://github.com/huggingface/transformers
💻 Usage Examples
Basic Usage
The model can be used with the following frameworks:
Notes:
temperature = 0.2 and top_p = 0.95 for chat completion (e.g. Audio Understanding) and temperature = 0.0 for transcription.- Multiple audios per message and multiple user turns with audio are supported.
- System prompts are not yet supported.
Advanced Usage
Transformers 🤗
Voxtral is supported in Transformers natively!
Audio Instruct
➡️ multi - audio + text instruction
from transformers import VoxtralForConditionalGeneration, AutoProcessor
import torch
device = "cuda"
repo_id = "MohamedRashad/Voxtral-Mini-3B-2507-transformers"
processor = AutoProcessor.from_pretrained(repo_id)
model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
conversation = [
{
"role": "user",
"content": [
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/mary_had_lamb.mp3",
},
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
},
{"type": "text", "text": "What sport and what nursery rhyme are referenced?"},
],
}
]
inputs = processor.apply_chat_template(conversation)
inputs = inputs.to(device, dtype=torch.bfloat16)
outputs = model.generate(**inputs, max_new_tokens=500)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print("\nGenerated response:")
print("=" * 80)
print(decoded_outputs[0])
print("=" * 80)
➡️ multi - turn
from transformers import VoxtralForConditionalGeneration, AutoProcessor
import torch
device = "cuda"
repo_id = "MohamedRashad/Voxtral-Mini-3B-2507-transformers"
processor = AutoProcessor.from_pretrained(repo_id)
model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
conversation = [
{
"role": "user",
"content": [
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3",
},
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3",
},
{"type": "text", "text": "Describe briefly what you can hear."},
],
},
{
"role": "assistant",
"content": "The audio begins with the speaker delivering a farewell address in Chicago, reflecting on his eight years as president and expressing gratitude to the American people. The audio then transitions to a weather report, stating that it was 35 degrees in Barcelona the previous day, but the temperature would drop to minus 20 degrees the following day.",
},
{
"role": "user",
"content": [
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
},
{"type": "text", "text": "Ok, now compare this new audio with the previous one."},
],
},
]
inputs = processor.apply_chat_template(conversation)
inputs = inputs.to(device, dtype=torch.bfloat16)
outputs = model.generate(**inputs, max_new_tokens=500)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print("\nGenerated response:")
print("=" * 80)
print(decoded_outputs[0])
print("=" * 80)
➡️ text only
from transformers import VoxtralForConditionalGeneration, AutoProcessor
import torch
device = "cuda"
repo_id = "MohamedRashad/Voxtral-Mini-3B-2507-transformers"
processor = AutoProcessor.from_pretrained(repo_id)
model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
conversation = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Why should AI models be open - sourced?",
},
],
}
]
inputs = processor.apply_chat_template(conversation)
inputs = inputs.to(device, dtype=torch.bfloat16)
outputs = model.generate(**inputs, max_new_tokens=500)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print("\nGenerated response:")
print("=" * 80)
print(decoded_outputs[0])
print("=" * 80)
➡️ audio only
from transformers import VoxtralForConditionalGeneration, AutoProcessor
import torch
device = "cuda"
repo_id = "MohamedRashad/Voxtral-Mini-3B-2507-transformers"
processor = AutoProcessor.from_pretrained(repo_id)
model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
conversation = [
{
"role": "user",
"content": [
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
},
],
}
]
inputs = processor.apply_chat_template(conversation)
inputs = inputs.to(device, dtype=torch.bfloat16)
outputs = model.generate(**inputs, max_new_tokens=500)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print("\nGenerated response:")
print("=" * 80)
print(decoded_outputs[0])
print("=" * 80)
➡️ batched inference
from transformers import VoxtralForConditionalGeneration, AutoProcessor
import torch
device = "cuda"
repo_id = "MohamedRashad/Voxtral-Mini-3B-2507-transformers"
processor = AutoProcessor.from_pretrained(repo_id)
model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
conversations = [
[
{
"role": "user",
"content": [
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3",
},
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3",
},
{
"type": "text",
"text": "Who's speaking in the speach and what city's weather is being discussed?",
},
],
}
],
[
{
"role": "user",
"content": [
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
},
{"type": "text", "text": "What can you tell me about this audio?"},
],
}
],
]
inputs = processor.apply_chat_template(conversations)
inputs = inputs.to(device, dtype=torch.bfloat16)
outputs = model.generate(**inputs, max_new_tokens=500)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print("\nGenerated responses:")
print("=" * 80)
for decoded_output in decoded_outputs:
print(decoded_output)
print("=" * 80)
Transcription
➡️ transcribe
from transformers import VoxtralForConditionalGeneration, AutoProcessor
import torch
device = "cuda"
repo_id = "MohamedRashad/Voxtral-Mini-3B-2507-transformers"
processor = AutoProcessor.from_pretrained(repo_id)
model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
inputs = processor.apply_transcrition_request(language="en", audio="https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3", model_id=repo_id)
inputs = inputs.to(device, dtype=torch.bfloat16)
outputs = model.generate(**inputs, max_new_tokens=500)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print("\nGenerated responses:")
print("=" * 80)
for decoded_output in decoded_outputs:
print(decoded_output)
print("=" * 80)
📄 License
This project is licensed under the Apache - 2.0 license.
⚠️ Important Note
If you want to learn more about how we process your personal data, please read our Privacy Policy.
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