Voxtral-Mini-3B-2507-transformers

Voxtral Mini 3B - 2507 (Transformers Edition)

Voxtral Mini is an enhancement of Ministral 3B, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding.

Learn more about Voxtral in our blog post here.

Key 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, Voxtral automatically predicts the source audio language and transcribes the text accordingly
• Long-form context: With a 32k token context length, Voxtral handles audios up to 30 minutes for transcription, or 40 minutes for understanding
• Built-in Q&A and summarization: Supports asking questions directly through audio. 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

Usage

The model can be used with the following frameworks;

• Transformers 🤗: See here

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

Transformers 🤗

Voxtral is supported in Transformers natively!

Install Transformers from source:

pip install git+https://github.com/huggingface/transformers

Audio Instruct

<details> <summary>➡️ multi-audio + text instruction</summary>

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)

</details> <details> <summary>➡️ multi-turn</summary>

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)

</details> <details> <summary>➡️ text only</summary>

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)

</details> <details> <summary>➡️ audio only</summary>

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)

</details> <details> <summary>➡️ batched inference</summary>

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)

</details>

Transcription

<details> <summary>➡️ transcribe</summary>

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)

</details>