asr_vad

NeMo ASR+VAD Inference

This example provides the ASR+VAD inference pipeline, with the option to perform only ASR or VAD alone.

Input

There are two types of input

• A manifest passed to manifest_filepath,
• A directory containing audios passed to audio_dir and also specify audio_type (default to wav).

The input manifest must be a manifest json file, where each line is a Python dictionary. The fields ["audio_filepath", "offset", "duration"] are required. An example of a manifest file is:

{"audio_filepath": "/path/to/audio_file1", "offset": 0, "duration": 10000}
{"audio_filepath": "/path/to/audio_file2", "offset": 0, "duration": 10000}

If you want to calculate WER, provide text in manifest as groundtruth. An example of a manifest file is:

{"audio_filepath": "/path/to/audio_file1", "offset": 0, "duration": 10000, "text": "hello world"}
{"audio_filepath": "/path/to/audio_file2", "offset": 0, "duration": 10000, "text": "hello world"}

Output

Output will be a folder storing the VAD predictions and/or a manifest containing the audio transcriptions. Some temporary data will also be stored.

Usage

To run the code with ASR+VAD default settings:

python speech_to_text_with_vad.py \
    manifest_filepath=/PATH/TO/MANIFEST.json \
    vad_model=vad_multilingual_frame_marblenet \
    asr_model=stt_en_conformer_ctc_large \
    vad_config=../conf/vad/frame_vad_infer_postprocess.yaml

• To use only ASR and disable VAD, set vad_model=None and use_rttm=False.

• To use only VAD, set asr_model=None and specify both vad_model and vad_config.

• To enable profiling, set profiling=True, but this will significantly slow down the program.

Using RTTM to handle non-speech audio segments

• To use or disable RTTM usage, set use_rttm to True or False. There are two options to use RTTM files, as specified by the parameter rttm_mode, which must be one of mask or drop. For mask, the RTTM file will be used to mask the non-speech features. For drop, the RTTM file will be used to drop the non-speech features.

• It's recommended that for rttm_mode='drop', use larger pad_onset and pad_offset to avoid dropping speech features.

• To use a specific value for feature masking, set feat_mask_val to the desired value. Default is feat_mask_val=None, where -16.530 (zero log mel-spectrogram value) will be used for post_norm and 0 (same as SpecAugment) will be used for pre_norm.

• To normalize feature before masking, set normalize=pre_norm, and set normalize=post_norm for masking before normalization.

Frame-VAD and Segment-VAD

• By default, speech_to_text_with_vad.py and vad_config=../conf/vad/frame_vad_infer_postprocess.yaml will use a frame-VAD model, which generates a speech/non-speech prediction for each audio frame of 20ms.
• To use segment-VAD, use speech_to_text_with_vad.py vad_type='segment' vad_config=../conf/vad/vad_inference_postprocessing.yaml instead. In segment-VAD, the audio is split into segments and VAD is performed on each segment. The segments are then stitched together to form the final output. The segment size and stride can be specified by window_length_in_sec and shift_length_in_sec in the VAD config (e.g., ../conf/vad/vad_inference_postprocessing.yaml) respectively. The default values are 0.63 seconds and 0.08 seconds respectively.

More options

• See more options in the InferenceConfig data class.