Cb1ock commited on
Commit
fc97aee
·
verified ·
1 Parent(s): c4b3988

Replace TTS-copied README with ASR-correct version (fix wrong-project badges, update model-index dataset id, drop placeholder image)

Browse files
Files changed (1) hide show
  1. README.md +13 -3
README.md CHANGED
@@ -26,7 +26,7 @@ model-index:
26
  type: automatic-speech-recognition
27
  dataset:
28
  name: Open ASR Leaderboard
29
- type: hf-audio/esb-datasets-test-only-sorted
30
  metrics:
31
  - type: wer
32
  value: 4.87
@@ -35,6 +35,16 @@ model-index:
35
 
36
  # MOSS-Transcribe-preview-2B
37
 
 
 
 
 
 
 
 
 
 
 
38
  MOSS-Transcribe-preview-2B is an English speech-to-text model that pairs a Qwen3-1.7B-base language-model backbone with a Qwen3-Omni-MoE audio encoder. A gated-MLP adapter projects audio features into the language-model embedding space. The model is trained on public English ASR corpora and fine-tuned with reinforcement learning on the Open ASR Leaderboard training splits.
39
 
40
  The model has approximately 2.4B parameters and is distributed as a single `bfloat16` safetensors shard of approximately 4.84 GB.
@@ -62,8 +72,8 @@ Evaluated on the [Open ASR Leaderboard](https://huggingface.co/spaces/hf-audio/o
62
  test sets. Predictions are produced with greedy decoding (`num_beams=1`,
63
  `max_new_tokens=512`), a single dataset-agnostic chat template, and scored with
64
  the leaderboard's standardized scoring (English normalizer + word-level edit
65
- distance with compound merging). TED-LIUM is no longer part of the leaderboard
66
- and is therefore excluded.
67
 
68
  | Dataset | WER (%) |
69
  |---|---|
 
26
  type: automatic-speech-recognition
27
  dataset:
28
  name: Open ASR Leaderboard
29
+ type: hf-audio/open-asr-leaderboard
30
  metrics:
31
  - type: wer
32
  value: 4.87
 
35
 
36
  # MOSS-Transcribe-preview-2B
37
 
38
+ <div align="center">
39
+
40
+ <a href="https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-preview-2B"><img src="https://img.shields.io/badge/HuggingFace-Model-yellow?logo=huggingface"></a>
41
+ <a href="https://huggingface.co/spaces/hf-audio/open_asr_leaderboard"><img src="https://img.shields.io/badge/Open%20ASR%20Leaderboard-Listing-blue?logo=huggingface"></a>
42
+ <a href="https://mosi.cn/#models"><img src="https://img.shields.io/badge/Blog-View-blue?logo=internet-explorer&amp"></a>
43
+ <a href="https://x.com/Open_MOSS"><img src="https://img.shields.io/badge/Twitter-Follow-black?logo=x&amp"></a>
44
+ <a href="https://discord.gg/fvm5TaWjU3"><img src="https://img.shields.io/badge/Discord-Join-5865F2?logo=discord&amp"></a>
45
+
46
+ </div>
47
+
48
  MOSS-Transcribe-preview-2B is an English speech-to-text model that pairs a Qwen3-1.7B-base language-model backbone with a Qwen3-Omni-MoE audio encoder. A gated-MLP adapter projects audio features into the language-model embedding space. The model is trained on public English ASR corpora and fine-tuned with reinforcement learning on the Open ASR Leaderboard training splits.
49
 
50
  The model has approximately 2.4B parameters and is distributed as a single `bfloat16` safetensors shard of approximately 4.84 GB.
 
72
  test sets. Predictions are produced with greedy decoding (`num_beams=1`,
73
  `max_new_tokens=512`), a single dataset-agnostic chat template, and scored with
74
  the leaderboard's standardized scoring (English normalizer + word-level edit
75
+ distance with compound merging). TED-LIUM is not currently part of the
76
+ leaderboard run and is therefore excluded.
77
 
78
  | Dataset | WER (%) |
79
  |---|---|