asrp 0.0.49

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asrp

ASR text preprocessing utility

install

pip install asrp

Preprocess

input: dictionary, with key sentence
output: preprocessed result, inplace handling.

import asrp

batch_data = {
    'sentence': "I'm fine, thanks."
}
asrp.fun_en(batch_data)

dynamic loading

import asrp

batch_data = {
    'sentence': "I'm fine, thanks."
}
preprocessor = getattr(asrp, 'fun_en')
preprocessor(batch_data)

Evaluation

import asrp

targets = ['HuggingFace is great!', 'Love Transformers!', 'Let\'s wav2vec!']
preds = ['HuggingFace is awesome!', 'Transformers is powerful.', 'Let\'s finetune wav2vec!']
print("chunk size WER: {:2f}".format(100 * asrp.chunked_wer(targets, preds, chunk_size=None)))
print("chunk size CER: {:2f}".format(100 * asrp.chunked_cer(targets, preds, chunk_size=None)))

Speech to Hubert code

import asrp

hc = asrp.HubertCode("facebook/hubert-large-ll60k", './km_feat_100_layer_20', 20)
hc('voice file path')

Hubert code to speech

import asrp

code = []  # discrete unit
# download tts checkpoint and waveglow_checkpint from https://github.com/pytorch/fairseq/tree/main/examples/textless_nlp/gslm/unit2speech
cs = asrp.Code2Speech(tts_checkpoint='./tts_checkpoint_best.pt', waveglow_checkpint='waveglow_256channels_new.pt')
cs(code)

# play on notebook
import IPython.display as ipd

ipd.Audio(data=cs(code), autoplay=False, rate=cs.sample_rate)

Speech Enhancement

Denoiser copied from fairseq

from asrp import SpeechEnhancer

ase = SpeechEnhancer()
print(ase('./test/xxx.wav'))

usage - liveASR

• modify from https://github.com/oliverguhr/wav2vec2-live

from asrp.live import LiveSpeech

english_model = "voidful/wav2vec2-xlsr-multilingual-56"
asr = LiveSpeech(english_model, device_name="default")
asr.start()

try:
    while True:
        text, sample_length, inference_time = asr.get_last_text()
        print(f"{sample_length:.3f}s"
              + f"\t{inference_time:.3f}s"
              + f"\t{text}")

except KeyboardInterrupt:
    asr.stop()

usage - liveASR - whisper

from asrp.live import LiveSpeech

whisper_model = "tiny"
asr = LiveSpeech(whisper_model)
asr.start()
last_text = ""
while True:
    asr_text = ""
    try:
        asr_text, sample_length, inference_time = asr.get_last_text()
        if len(asr_text) > 0:
            print(asr_text, sample_length, inference_time)
    except KeyboardInterrupt:
        asr.stop()
        break