---
title: mms-300m-1130-forced-aligner
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/mms-300m-1130-forced-aligner"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/mms-300m-1130-forced-aligner.md"
repository: AI-ModelScope/mms-300m-1130-forced-aligner
last_updated: 2026-04-15
license: cc-by-nc-4.0
model_type:
  - wav2vec2
architectures:
  - Wav2Vec2ForCTC
parameters: 315.5M
tensor_type:
  - F32
library_name:
  - pytorch
  - safetensors
frameworks:
  - pytorch
language:
  - ab
  - af
  - ak
  - am
  - ar
  - as
  - av
  - ay
  - az
  - ba
  - bm
  - be
  - bn
  - bi
  - bo
  - sh
  - br
  - bg
  - ca
  - cs
  - ce
  - cv
  - ku
  - cy
  - da
  - de
  - dv
  - dz
  - el
  - en
  - eo
  - et
  - eu
  - ee
  - fo
  - fa
  - fj
  - fi
  - fr
  - fy
  - ff
  - ga
  - gl
  - gn
  - gu
  - zh
  - ht
  - ha
  - he
  - hi
  - sh
  - hu
  - hy
  - ig
  - ia
  - ms
  - is
  - it
  - jv
  - ja
  - kn
  - ka
  - kk
  - kr
  - km
  - ki
  - rw
  - ky
  - ko
  - kv
  - lo
  - la
  - lv
  - ln
  - lt
  - lb
  - lg
  - mh
  - ml
  - mr
  - ms
  - mk
  - mg
  - mt
  - mn
  - mi
  - my
  - zh
  - nl
  - no
  - no
  - ne
  - ny
  - oc
  - om
  - or
  - os
  - pa
  - pl
  - pt
  - ms
  - ps
  - qu
  - qu
  - qu
  - qu
  - qu
  - qu
  - qu
  - qu
  - qu
  - qu
  - qu
  - qu
  - qu
  - qu
  - qu
  - qu
  - qu
  - qu
  - qu
  - qu
  - qu
  - qu
  - ro
  - rn
  - ru
  - sg
  - sk
  - sl
  - sm
  - sn
  - sd
  - so
  - es
  - sq
  - su
  - sv
  - sw
  - ta
  - tt
  - te
  - tg
  - tl
  - th
  - ti
  - ts
  - tr
  - uk
  - ms
  - vi
  - wo
  - xh
  - ms
  - yo
  - ms
  - zu
  - za
downloads: 438
stars: 1
tags:
  - mms
  - wav2vec2
  - audio
  - voice
  - speech
  - forced-alignment
---

# mms-300m-1130-forced-aligner

> mms-300m-1130-forced-aligner - AI-ModelScope 在 ModelScope 开源的模型。Forced Alignment with Hugging Face CTC Models This Python package provides an efficient way to perform forced alignment between text and audio using Hugging Face's pretrained models. it also…

AI-ModelScope/mms-300m-1130-forced-aligner 是 ModelScope 魔搭社区上的 315.5M 参数机器学习模型，采用 cc-by-nc-4.0 许可。

- **Repository**: AI-ModelScope/mms-300m-1130-forced-aligner
- **License**: cc-by-nc-4.0
- **Parameters**: 315.5M
- **Tags**: mms, wav2vec2, audio, voice, speech, forced-alignment
- **Downloads**: 438
- **Stars**: 1
- **Last updated**: 2026-04-15

Source: https://www.modelscope.cn/models/AI-ModelScope/mms-300m-1130-forced-aligner

---

# Forced Alignment with Hugging Face CTC Models
This Python package provides an efficient way to perform forced alignment between text and audio using Hugging Face's pretrained models. it also features an improved implementation to use much less memory than TorchAudio forced alignment API.

The model checkpoint uploaded here is a conversion from torchaudio to HF Transformers for the MMS-300M checkpoint trained on forced alignment dataset

## Installation

```bash
pip install git+https://github.com/MahmoudAshraf97/ctc-forced-aligner.git
```
## Usage

```python
import torch
from ctc_forced_aligner import (
    load_audio,
    load_alignment_model,
    generate_emissions,
    preprocess_text,
    get_alignments,
    get_spans,
    postprocess_results,
)

audio_path = "your/audio/path"
text_path = "your/text/path"
language = "iso" # ISO-639-3 Language code
device = "cuda" if torch.cuda.is_available() else "cpu"
batch_size = 16


alignment_model, alignment_tokenizer = load_alignment_model(
    device,
    dtype=torch.float16 if device == "cuda" else torch.float32,
)

audio_waveform = load_audio(audio_path, alignment_model.dtype, alignment_model.device)


with open(text_path, "r") as f:
    lines = f.readlines()
text = "".join(line for line in lines).replace("\n", " ").strip()

emissions, stride = generate_emissions(
    alignment_model, audio_waveform, batch_size=batch_size
)

tokens_starred, text_starred = preprocess_text(
    text,
    romanize=True,
    language=language,
)

segments, scores, blank_token = get_alignments(
    emissions,
    tokens_starred,
    alignment_tokenizer,
)

spans = get_spans(tokens_starred, segments, blank_token)

word_timestamps = postprocess_results(text_starred, spans, stride, scores)
```