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Project description
ASRP: Automatic Speech Recognition Preprocessing Utility
ASRP is a python package that offers a set of tools to preprocess and evaluate ASR (Automatic Speech Recognition) text. The package also provides a speech-to-text transcription tool and a text-to-speech conversion tool. The code is open-source and can be installed using pip.
Key Features
- Preprocess ASR text with ease
- Evaluate ASR output quality
- Transcribe speech to Hubert code
- Convert unit code to speech
- Enhance speech quality with a noise reduction tool
- LiveASR tool for real-time speech recognition
- Speaker Embedding Extraction (x-vector/d-vector)
install
pip install asrp
Preprocess
ASRP offers an easy-to-use set of functions to preprocess ASR text data.
The input data is a dictionary with the key 'sentence', and the output is the preprocessed text.
You can either use the fun_en function or use dynamic loading. Here's how to use it:
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
ASRP provides functions to evaluate the output quality of ASR systems using
the Word Error Rate (WER) and Character Error Rate (CER) metrics.
Here's how to use it:
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 Discrete Unit
import asrp
import nlp2
# https://github.com/facebookresearch/fairseq/blob/ust/examples/speech_to_speech/docs/textless_s2st_real_data.md
# https://github.com/facebookresearch/fairseq/tree/main/examples/textless_nlp/gslm/ulm
nlp2.download_file(
'https://huggingface.co/voidful/mhubert-base/resolve/main/mhubert_base_vp_en_es_fr_it3_L11_km1000.bin', './')
hc = asrp.HubertCode("voidful/mhubert-base", './mhubert_base_vp_en_es_fr_it3_L11_km1000.bin', 11,
chunk_sec=30,
worker=20)
hc('voice file path')
Discrete Unit to speech
import asrp
code = [] # discrete unit
# https://github.com/pytorch/fairseq/tree/main/examples/textless_nlp/gslm/unit2speech
# https://github.com/facebookresearch/fairseq/blob/ust/examples/speech_to_speech/docs/textless_s2st_real_data.md
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)
mhubert English hifigan vocoder example
import asrp
import nlp2
import IPython.display as ipd
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
nlp2.download_file(
'https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj/g_00500000',
'./')
tokenizer = AutoTokenizer.from_pretrained("voidful/mhubert-unit-tts")
model = AutoModelForSeq2SeqLM.from_pretrained("voidful/mhubert-unit-tts")
model.eval()
cs = asrp.Code2Speech(tts_checkpoint='./g_00500000', vocoder='hifigan')
inputs = tokenizer(["The quick brown fox jumps over the lazy dog."], return_tensors="pt")
code = tokenizer.batch_decode(model.generate(**inputs,max_length=1024))[0]
code = [int(i) for i in code.replace("</s>","").replace("<s>","").split("v_tok_")[1:]]
print(code)
ipd.Audio(data=cs(code), autoplay=False, rate=cs.sample_rate)
Speech Enhancement
ASRP also provides a tool to enhance speech quality with a noise reduction tool.
from https://github.com/facebookresearch/fairseq/tree/main/examples/speech_synthesis/preprocessing/denoiser
from asrp import SpeechEnhancer
ase = SpeechEnhancer()
print(ase('./test/xxx.wav'))
LiveASR - huggingface's model
- 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()
LiveASR - whisper's model
from asrp.live import LiveSpeech
whisper_model = "tiny"
asr = LiveSpeech(whisper_model, vad_mode=2, language='zh')
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
Speaker Embedding Extraction - x vector
from https://speechbrain.readthedocs.io/en/latest/API/speechbrain.lobes.models.Xvector.html
from asrp.speaker_embedding import extract_x_vector
extract_x_vector('./test/xxx.wav')
Speaker Embedding Extraction - d vector
from https://github.com/yistLin/dvector
from asrp.speaker_embedding import extract_d_vector
extract_d_vector('./test/xxx.wav')
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