Audio-Text-to-Text
Transformers
Safetensors
English
Korean
fastslm
feature-extraction
audio
text-generation
custom_code
Eval Results
Instructions to use okestro-ai-lab/FastSLM-ASR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use okestro-ai-lab/FastSLM-ASR with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("okestro-ai-lab/FastSLM-ASR", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -28,7 +28,7 @@ model-index:
|
|
| 28 |
metrics:
|
| 29 |
- name: Test WER
|
| 30 |
type: wer
|
| 31 |
-
value: 9.
|
| 32 |
- task:
|
| 33 |
name: Automatic Speech Recognition
|
| 34 |
type: automatic-speech-recognition
|
|
@@ -41,7 +41,7 @@ model-index:
|
|
| 41 |
metrics:
|
| 42 |
- name: Test WER
|
| 43 |
type: wer
|
| 44 |
-
value:
|
| 45 |
- task:
|
| 46 |
name: Automatic Speech Recognition
|
| 47 |
type: automatic-speech-recognition
|
|
@@ -54,7 +54,7 @@ model-index:
|
|
| 54 |
metrics:
|
| 55 |
- name: Test WER
|
| 56 |
type: wer
|
| 57 |
-
value: 9.
|
| 58 |
- task:
|
| 59 |
name: Automatic Speech Recognition
|
| 60 |
type: automatic-speech-recognition
|
|
@@ -68,7 +68,7 @@ model-index:
|
|
| 68 |
metrics:
|
| 69 |
- name: Test WER
|
| 70 |
type: wer
|
| 71 |
-
value: 1.
|
| 72 |
- task:
|
| 73 |
name: Automatic Speech Recognition
|
| 74 |
type: automatic-speech-recognition
|
|
@@ -82,7 +82,7 @@ model-index:
|
|
| 82 |
metrics:
|
| 83 |
- name: Test WER
|
| 84 |
type: wer
|
| 85 |
-
value: 4.
|
| 86 |
- task:
|
| 87 |
name: Automatic Speech Recognition
|
| 88 |
type: automatic-speech-recognition
|
|
@@ -96,7 +96,7 @@ model-index:
|
|
| 96 |
metrics:
|
| 97 |
- name: Test WER
|
| 98 |
type: wer
|
| 99 |
-
value: 6.
|
| 100 |
- task:
|
| 101 |
type: Automatic Speech Recognition
|
| 102 |
name: automatic-speech-recognition
|
|
@@ -110,7 +110,7 @@ model-index:
|
|
| 110 |
metrics:
|
| 111 |
- name: Test WER
|
| 112 |
type: wer
|
| 113 |
-
value: 3.
|
| 114 |
- task:
|
| 115 |
type: Automatic Speech Recognition
|
| 116 |
name: automatic-speech-recognition
|
|
@@ -124,7 +124,7 @@ model-index:
|
|
| 124 |
metrics:
|
| 125 |
- name: Test WER
|
| 126 |
type: wer
|
| 127 |
-
value: 2.
|
| 128 |
---
|
| 129 |
|
| 130 |
# 🚀 FastSLM: Hierarchical Frame Q-Former for Effective Speech Modality Adaptation
|
|
|
|
| 28 |
metrics:
|
| 29 |
- name: Test WER
|
| 30 |
type: wer
|
| 31 |
+
value: 9.55
|
| 32 |
- task:
|
| 33 |
name: Automatic Speech Recognition
|
| 34 |
type: automatic-speech-recognition
|
|
|
|
| 41 |
metrics:
|
| 42 |
- name: Test WER
|
| 43 |
type: wer
|
| 44 |
+
value: 10.27
|
| 45 |
- task:
|
| 46 |
name: Automatic Speech Recognition
|
| 47 |
type: automatic-speech-recognition
|
|
|
|
| 54 |
metrics:
|
| 55 |
- name: Test WER
|
| 56 |
type: wer
|
| 57 |
+
value: 9.89
|
| 58 |
- task:
|
| 59 |
name: Automatic Speech Recognition
|
| 60 |
type: automatic-speech-recognition
|
|
|
|
| 68 |
metrics:
|
| 69 |
- name: Test WER
|
| 70 |
type: wer
|
| 71 |
+
value: 1.93
|
| 72 |
- task:
|
| 73 |
name: Automatic Speech Recognition
|
| 74 |
type: automatic-speech-recognition
|
|
|
|
| 82 |
metrics:
|
| 83 |
- name: Test WER
|
| 84 |
type: wer
|
| 85 |
+
value: 4.43
|
| 86 |
- task:
|
| 87 |
name: Automatic Speech Recognition
|
| 88 |
type: automatic-speech-recognition
|
|
|
|
| 96 |
metrics:
|
| 97 |
- name: Test WER
|
| 98 |
type: wer
|
| 99 |
+
value: 6.33
|
| 100 |
- task:
|
| 101 |
type: Automatic Speech Recognition
|
| 102 |
name: automatic-speech-recognition
|
|
|
|
| 110 |
metrics:
|
| 111 |
- name: Test WER
|
| 112 |
type: wer
|
| 113 |
+
value: 3.41
|
| 114 |
- task:
|
| 115 |
type: Automatic Speech Recognition
|
| 116 |
name: automatic-speech-recognition
|
|
|
|
| 124 |
metrics:
|
| 125 |
- name: Test WER
|
| 126 |
type: wer
|
| 127 |
+
value: 2.27
|
| 128 |
---
|
| 129 |
|
| 130 |
# 🚀 FastSLM: Hierarchical Frame Q-Former for Effective Speech Modality Adaptation
|