Voxtral-Mini-3B-2507: transcribe.cpp GGUF
GGUF conversions of mistralai/Voxtral-Mini-3B-2507 for use
with transcribe.cpp.
Ported from upstream commit
3060fe3,
pinned 2026-06-06.
Validated against the Transformers reference at transcribe.cpp commit
483c122
on 2026-06-06.
Offline audio-LLM speech-to-text and speech translation. A Whisper-large-v3
bidirectional audio encoder feeds a 4-frame-group projector (375 audio tokens
per 30 s chunk) into a Ministral-3B causal LM (30 layers, GQA 32/8, NEOX RoPE,
SwiGLU) via audio-token injection. Takes a 16 kHz mono WAV and produces a
transcript via greedy decoding; speech translation runs through the
mistral-common instruct template. The smaller sibling of Voxtral Small 24B —
same encoder, projector, log-mel frontend, and tekken tokenizer, with a 3B
decoder in place of Mistral-Small-24B.
Downloads
WER measured on the full LibriSpeech test-clean split (2620 utterances) with
the Whisper English text normalizer, batch size 8 on an NVIDIA L40S.
Same-machine HuggingFace transformers reference
(VoxtralForConditionalGeneration, BF16, attn_implementation=eager, greedy):
1.87%; the BF16 GGUF matches within rounding. The BF16-vs-reference parity is
the family's tensor-level numerical gate — 43 checkpointed tensors within
tolerance, transcript byte-exact.
Usage
Build transcribe.cpp from source:
git clone [email protected]:handy-computer/transcribe.cpp.git
cd transcribe.cpp
cmake -B build && cmake --build build
Run on a 16 kHz mono WAV:
build/bin/transcribe-cli \
-m Voxtral-Mini-3B-2507-Q8_0.gguf \
input.wav
If your audio isn't already 16 kHz mono WAV, convert it first:
ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav
See the transcribe.cpp model page for performance
numbers, numerical validation, and reproduction steps.
License
Inherited from the base model: Apache-2.0. See the
upstream model card for full terms.
Original Model Card
The section below is reproduced from
mistralai/Voxtral-Mini-3B-2507 at commit
3060fe3 for offline reference. The upstream card is the
authoritative source.
Voxtral Mini 1.0 (3B) - 2507
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 and our research paper.
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;
- vllm (recommended): See here
- 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
vLLM (recommended)
We recommend using this model with vLLM.
Installation
Make sure to install vllm >= 0.10.0, we recommend using uv:
uv pip install -U "vllm[audio]" --system
Doing so should automatically install mistral_common >= 1.8.1.
To check:
python -c "import mistral_common; print(mistral_common.__version__)"
Offline
You can test that your vLLM setup works as expected by cloning the vLLM repo:
git clone https://github.com/vllm-project/vllm && cd vllm
and then running:
python examples/offline_inference/audio_language.py --num-audios 2 --model-type voxtral
Serve
We recommend that you use Voxtral-Small-24B-2507 in a server/client setting.
- Spin up a server:
vllm serve mistralai/Voxtral-Mini-3B-2507 --tokenizer_mode mistral --config_format mistral --load_format mistral
Note: Running Voxtral-Mini-3B-2507 on GPU requires ~9.5 GB of GPU RAM in bf16 or fp16.
- To ping the client you can use a simple Python snippet. See the following examples.
Audio Instruct
Leverage the audio capabilities of Voxtral-Mini-3B-2507 to chat.
Make sure that your client has mistral-common with audio installed:
pip install --upgrade mistral_common\[audio\]
Python snippet
from mistral_common.protocol.instruct.messages import TextChunk, AudioChunk, UserMessage, AssistantMessage, RawAudio
from mistral_common.audio import Audio
from huggingface_hub import hf_hub_download
from openai import OpenAI
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://<your-server-host>:8000/v1"
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
obama_file = hf_hub_download("patrickvonplaten/audio_samples", "obama.mp3", repo_type="dataset")
bcn_file = hf_hub_download("patrickvonplaten/audio_samples", "bcn_weather.mp3", repo_type="dataset")
def file_to_chunk(file: str) -> AudioChunk:
audio = Audio.from_file(file, strict=False)
return AudioChunk.from_audio(audio)
text_chunk = TextChunk(text="Which speaker is more inspiring? Why? How are they different from each other?")
user_msg = UserMessage(content=[file_to_chunk(obama_file), file_to_chunk(bcn_file), text_chunk]).to_openai()
print(30 * "=" + "USER 1" + 30 * "=")
print(text_chunk.text)
print("\n\n")
response = client.chat.completions.create(
model=model,
messages=[user_msg],
temperature=0.2,
top_p=0.95,
)
content = response.choices[0].message.content
print(30 * "=" + "BOT 1" + 30 * "=")
print(content)
print("\n\n")
# The speaker who is more inspiring is the one who delivered the farewell address, as they express
# gratitude, optimism, and a strong commitment to the nation and its citizens. They emphasize the importance of
# self-government and active citizenship, encouraging everyone to participate in the democratic process. In contrast,
# the other speaker provides a factual update on the weather in Barcelona, which is less inspiring as it
# lacks the emotional and motivational content of the farewell address.
# **Differences:**
# - The farewell address speaker focuses on the values and responsibilities of citizenship, encouraging active participation in democracy.
# - The weather update speaker provides factual information about the temperature in Barcelona, without any emotional or motivational content.
messages = [
user_msg,
AssistantMessage(content=content).to_openai(),
UserMessage(content="Ok, now please summarize the content of the first audio.").to_openai()
]
print(30 * "=" + "USER 2" + 30 * "=")
print(messages[-1]["content"])
print("\n\n")
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=0.2,
top_p=0.95,
)
content = response.choices[0].message.content
print(30 * "=" + "BOT 2" + 30 * "=")
print(content)
Transcription
Voxtral-Mini-3B-2507 has powerful transcription capabilities!
Make sure that your client has mistral-common with audio installed:
pip install --upgrade mistral_common\[audio\]
Python snippet
from mistral_common.protocol.transcription.request import TranscriptionRequest
from mistral_common.protocol.instruct.messages import RawAudio
from mistral_common.audio import Audio
from huggingface_hub import hf_hub_download
from openai import OpenAI
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://<your-server-host>:8000/v1"
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
obama_file = hf_hub_download("patrickvonplaten/audio_samples", "obama.mp3", repo_type="dataset")
audio = Audio.from_file(obama_file, strict=False)
audio = RawAudio.from_audio(audio)
req = TranscriptionRequest(model=model, audio=audio, language="en", temperature=0.0).to_openai(exclude=("top_p", "seed"))
response = client.audio.transcriptions.create(**req)
print(response)
Transformers
Starting with transformers >= 4.54.0 and above, you can run Voxtral natively!
Install Transformers:
pip install -U transformers
Make sure to have mistral-common >= 1.8.1 installed with audio dependencies:
pip install --upgrade "mistral-common[audio]"
Audio Instruct
multi-audio + text instruction
from transformers import VoxtralForConditionalGeneration, AutoProcessor
import torch
device = "cuda"
repo_id = "mistralai/Voxtral-Mini-3B-2507"
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 = "mistralai/Voxtral-Mini-3B-2507"
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 = "mistralai/Voxtral-Mini-3B-2507"
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 = "mistralai/Voxtral-Mini-3B-2507"
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 = "mistralai/Voxtral-Mini-3B-2507"
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 = "mistralai/Voxtral-Mini-3B-2507"
processor = AutoProcessor.from_pretrained(repo_id)
model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
inputs = processor.apply_transcription_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)
