| | |
|---|
| | | batch_size=1 |
|---|
| | | ) |
|---|
| | | audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx)) |
|---|
| | | inference_pipline(audio_in=audio_in) |
|---|
| | | inference_pipline(audio_in=audio_in, param_dict={"decoding_model": "normal"}) |
|---|
| | | |
|---|
| | | |
|---|
| | | def modelscope_infer(params): |
|---|
| | |
|---|
| | | # If text exists, compute CER |
|---|
| | | text_in = os.path.join(params["data_dir"], "text") |
|---|
| | | if os.path.exists(text_in): |
|---|
| | | text_proc_file = os.path.join(best_recog_path, "token") |
|---|
| | | text_proc_file = os.path.join(best_recog_path, "text") |
|---|
| | | compute_wer(text_in, text_proc_file, os.path.join(best_recog_path, "text.cer")) |
|---|
| | | |
|---|
| | | |
|---|
