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ONNX
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  1. .gitattributes +268 -0
  2. README.md +84 -0
  3. __pycache__/model_onnx.cpython-312.pyc +0 -0
  4. assets/Constant_1970_attr__value +3 -0
  5. assets/ctc_decoder.onnx +3 -0
  6. assets/encoder.onnx +3 -0
  7. assets/joint_enc.onnx +3 -0
  8. assets/joint_post_net_as.onnx +3 -0
  9. assets/joint_post_net_bn.onnx +3 -0
  10. assets/joint_post_net_brx.onnx +3 -0
  11. assets/joint_post_net_doi.onnx +3 -0
  12. assets/joint_post_net_gu.onnx +3 -0
  13. assets/joint_post_net_hi.onnx +3 -0
  14. assets/joint_post_net_kn.onnx +3 -0
  15. assets/joint_post_net_kok.onnx +3 -0
  16. assets/joint_post_net_ks.onnx +3 -0
  17. assets/joint_post_net_mai.onnx +3 -0
  18. assets/joint_post_net_ml.onnx +3 -0
  19. assets/joint_post_net_mni.onnx +3 -0
  20. assets/joint_post_net_mr.onnx +3 -0
  21. assets/joint_post_net_ne.onnx +3 -0
  22. assets/joint_post_net_or.onnx +3 -0
  23. assets/joint_post_net_pa.onnx +3 -0
  24. assets/joint_post_net_sa.onnx +3 -0
  25. assets/joint_post_net_sat.onnx +3 -0
  26. assets/joint_post_net_sd.onnx +3 -0
  27. assets/joint_post_net_ta.onnx +3 -0
  28. assets/joint_post_net_te.onnx +3 -0
  29. assets/joint_post_net_ur.onnx +3 -0
  30. assets/joint_pre_net.onnx +3 -0
  31. assets/joint_pred.onnx +3 -0
  32. assets/language_masks.json +0 -0
  33. assets/layers.0.conv.pointwise_conv1.bias +0 -0
  34. assets/layers.0.conv.pointwise_conv1.weight +3 -0
  35. assets/layers.0.conv.pointwise_conv2.weight +3 -0
  36. assets/layers.0.feed_forward1.linear1.bias +0 -0
  37. assets/layers.0.feed_forward2.linear1.bias +0 -0
  38. assets/layers.1.conv.pointwise_conv1.bias +0 -0
  39. assets/layers.1.conv.pointwise_conv1.weight +3 -0
  40. assets/layers.1.conv.pointwise_conv2.weight +3 -0
  41. assets/layers.1.feed_forward1.linear1.bias +0 -0
  42. assets/layers.1.feed_forward2.linear1.bias +0 -0
  43. assets/layers.10.conv.pointwise_conv1.bias +0 -0
  44. assets/layers.10.conv.pointwise_conv1.weight +3 -0
  45. assets/layers.10.conv.pointwise_conv2.weight +3 -0
  46. assets/layers.10.feed_forward1.linear1.bias +0 -0
  47. assets/layers.10.feed_forward2.linear1.bias +0 -0
  48. assets/layers.11.conv.pointwise_conv1.bias +0 -0
  49. assets/layers.11.conv.pointwise_conv1.weight +3 -0
  50. assets/layers.11.conv.pointwise_conv2.weight +3 -0
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README.md ADDED
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1
+ ---
2
+ license: mit
3
+ ---
4
+ # **IndicConformer**
5
+ AI4Bharat's IndicConformers is a suite of ASR models built to deliver accurate speech-to-text conversion in all 22 official Indian languages. By leveraging cutting-edge deep learning techniques, these models provide precise transcriptions. As the country's first open-source ASR system covering such a vast array of languages, AI4Bharat Indic Conformer is a transformative tool for making technology more inclusive and accessible to all. IndicConformer is released under the MIT license.
6
+
7
+ ## **Model Details**
8
+ - **Model Name:** IndicConformer-600M-Multi
9
+ - **Repository:** [ai4bharat/indic-conformer-600m-multilingual](https://huggingface.co/ai4bharat/indic-conformer-600m-multilingual)
10
+ - **Architecture:** Multilingual Conformer-based Hybrid CTC + RNNT ASR model
11
+ - **Parameter Size:** 600M
12
+ - **Languages Supported:** IN-22
13
+
14
+ ---
15
+
16
+ ## **Model Usage**
17
+ This model can be used to transcribe speech in various Indian languages. It supports two decoding strategies:
18
+ - **CTC (Connectionist Temporal Classification)**
19
+ - **RNNT (Recurrent Neural Network Transducer)**
20
+
21
+ ### **Installation**
22
+ Ensure that you have `transformers` and `torchaudio` installed:
23
+ ```bash
24
+ pip install transformers torchaudio onnx onnxruntime onnxruntime-gpu
25
+ ```
26
+
27
+ ### **Inference Example**
28
+ ```python
29
+ from transformers import AutoModel
30
+ import torch, torchaudio
31
+
32
+ # Load the model
33
+ model = AutoModel.from_pretrained("ai4bharat/indic-conformer-600m-multilingual", trust_remote_code=True)
34
+
35
+ # Load an audio file
36
+ wav, sr = torchaudio.load("audio.flac")
37
+ wav = torch.mean(wav, dim=0, keepdim=True)
38
+
39
+ target_sample_rate = 16000 # Expected sample rate
40
+ if sr != target_sample_rate:
41
+ resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sample_rate)
42
+ wav = resampler(wav)
43
+
44
+ # Perform ASR with CTC decoding
45
+ transcription_ctc = model(wav, "hi", "ctc")
46
+ print("CTC Transcription:", transcription_ctc)
47
+
48
+ # Perform ASR with RNNT decoding
49
+ transcription_rnnt = model(wav, "hi", "rnnt")
50
+ print("RNNT Transcription:", transcription_rnnt)
51
+ ```
52
+
53
+ ## **Supported Languages**
54
+ IndicConformer-600M-Multi is trained for **22 officially recognized languages of India**, including:
55
+ - Assamese(`as`)
56
+ - Bengali(`bn`)
57
+ - Bodo(`brx`)
58
+ - Dogri(`doi`)
59
+ - Gujarati(`gu`)
60
+ - Hindi(`hi`)
61
+ - Kannada(`kn`)
62
+ - Konkani(`kok`)
63
+ - Kashmiri(`ks`)
64
+ - Maithili(`mai`)
65
+ - Malayalam(`ml`)
66
+ - Manipuri(`mni`)
67
+ - Marathi(`mr`)
68
+ - Nepali(`ne`)
69
+ - Odia(`or`)
70
+ - Punjabi(`pa`)
71
+ - Sanskrit(`sa`)
72
+ - Santali(`sat`)
73
+ - Sindhi(`sd`)
74
+ - Tamil(`ta`)
75
+ - Telugu(`te`)
76
+ - Urdu(`ur`)
77
+
78
+ The tokenizers used for these languages can be found [here](https://github.com/AI4Bharat/IndicVoices/tree/master/artifacts/tokenizers)
79
+
80
+ ## **Contact**
81
+ For any questions or feedback, please contact:
82
+
83
+ - Tahir Javed (tahir@cse.iitm.ac.in)
84
+ - Kaushal Bhogale (CS22D006@cse.iitm.ac.in)
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