| | |
|---|
| | | batch_size=1 |
|---|
| | | ) |
|---|
| | | audio_in = os.path.join(params["data_dir"], "wav.scp") |
|---|
| | | inference_pipeline(audio_in=audio_in) |
|---|
| | | inference_pipeline(audio_in=audio_in, param_dict={"decoding_model": "offline"}) |
|---|
| | | |
|---|
| | | # computer CER if GT text is set |
|---|
| | | text_in = os.path.join(params["data_dir"], "text") |
|---|
| | |
|---|
| | | params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"] |
|---|
| | | params["output_dir"] = "./checkpoint" |
|---|
| | | params["data_dir"] = "./data/test" |
|---|
| | | params["decoding_model_name"] = "20epoch.pth" |
|---|
| | | params["decoding_model_name"] = "20epoch.pb" |
|---|
| | | modelscope_infer_after_finetune(params) |
|---|
