Automatic Speech Recognition
NeMo
Safetensors
indic_canary
speech
audio
asr
multilingual
indic
code-switching
code-mixing
romanization
transliteration
language-identification
canary
fastconformer
custom_code
Instructions to use spark-ux/indic-transcribe-flex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use spark-ux/indic-transcribe-flex with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("spark-ux/indic-transcribe-flex") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "IndicCanaryForConditionalGeneration" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_indic_canary.IndicCanaryConfig", | |
| "AutoModel": "modeling_indic_canary.IndicCanaryForConditionalGeneration", | |
| "AutoModelForSpeechSeq2Seq": "modeling_indic_canary.IndicCanaryForConditionalGeneration" | |
| }, | |
| "bos_token_id": 4, | |
| "conv_kernel_size": 9, | |
| "d_model": 1024, | |
| "decoder_attention_heads": 8, | |
| "decoder_ffn_dim": 4096, | |
| "decoder_layers": 24, | |
| "decoder_start_token_id": 7, | |
| "encoder_attention_heads": 8, | |
| "encoder_ffn_dim": 4096, | |
| "encoder_layers": 32, | |
| "eos_token_id": 3, | |
| "is_encoder_decoder": true, | |
| "max_generation_delta": 50, | |
| "max_target_positions": 1024, | |
| "model_type": "indic_canary", | |
| "num_mel_bins": 128, | |
| "pad_token_id": 2, | |
| "subsampling_conv_channels": 256, | |
| "subsampling_factor": 8, | |
| "tie_word_embeddings": true, | |
| "torch_dtype": "float32", | |
| "vocab_size": 7152 | |
| } |