File size: 2,403 Bytes
16bd0f8 2e4e29b 16bd0f8 2e4e29b 16bd0f8 2e4e29b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
---
library_name: mlx-audio-plus
base_model:
- FunAudioLLM/Fun-ASR-Nano-2512
tags:
- mlx
- funasr
- speech-recognition
- speech-to-text
- stt
pipeline_tag: automatic-speech-recognition
language:
- multilingual
---
# mlx-community/Fun-ASR-Nano-2512-4bit
This model was converted to MLX format from [FunAudioLLM/Fun-ASR-Nano-2512](https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512) using [mlx-audio-plus](https://github.com/DePasqualeOrg/mlx-audio-plus) version **0.1.4**.
## Features
| Feature | Description |
|---------|-------------|
| **Multilingual** | Supports 13+ languages |
| **Translation** | Translate speech directly to English text |
| **Custom prompting** | Guide recognition with domain-specific context |
| **Streaming** | Real-time token-by-token output |
## Installation
```bash
pip install -U mlx-audio-plus
```
## Usage
### Basic Transcription
```python
from mlx_audio.stt.models.funasr import Model
# Load the model
model = Model.from_pretrained("mlx-community/Fun-ASR-Nano-2512-4bit")
# Transcribe audio
result = model.generate("audio.wav")
print(result.text)
# Output: "The quick brown fox jumps over the lazy dog."
print(f"Duration: {result.duration:.2f}s")
print(f"Language: {result.language}")
```
### Translation (Speech to English Text)
```python
# Translate Chinese/Japanese/etc. audio to English
result = model.generate(
"chinese_speech.wav",
task="translate",
target_language="en"
)
print(result.text) # English translation
```
### Custom Prompting
Provide context to improve recognition accuracy for specialized domains:
```python
# Medical transcription
result = model.generate(
"doctor_notes.wav",
initial_prompt="Medical consultation discussing cardiac symptoms and treatment options."
)
# Technical content
result = model.generate(
"tech_podcast.wav",
initial_prompt="Discussion about machine learning, APIs, and software development."
)
```
### Streaming Output
Get real-time output as the model generates:
```python
# Print tokens as they're generated
result = model.generate("audio.wav", verbose=True)
# Tokens stream to stdout in real-time
# Or use the streaming generator
for chunk in model.generate("audio.wav", stream=True):
print(chunk, end="", flush=True)
```
## Supported Languages
See [original model](https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512) for the full list of supported languages.
|