# 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](#preprocess)
- [Evaluate ASR output quality](#Evaluation)
- [Transcribe speech to Hubert code](#speech-to-discrete-unit)
- [Convert unit code to speech](#discrete-unit-to-speech)
- [Enhance speech quality with a noise reduction tool](#speech-enhancement)
- [LiveASR tool for real-time speech recognition](#liveasr---huggingfaces-model)
- [Speaker Embedding Extraction (x-vector/d-vector)](#speaker-embedding-extraction---x-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:
```python
import asrp
batch_data = {
'sentence': "I'm fine, thanks."
}
asrp.fun_en(batch_data)
```
dynamic loading
```python
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:
```python
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
```python
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
```python
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
```python
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
```python
from asrp import SpeechEnhancer
ase = SpeechEnhancer()
print(ase('./test/xxx.wav'))
```
## LiveASR - huggingface's model
* modify from https://github.com/oliverguhr/wav2vec2-live
```python
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
```python
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
```python
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
```python
from asrp.speaker_embedding import extract_d_vector
extract_d_vector('./test/xxx.wav')
```
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"description": "# ASRP: Automatic Speech Recognition Preprocessing Utility\n\nASRP is a python package that offers a set of tools to preprocess and evaluate ASR (Automatic Speech Recognition) text.\nThe package also provides a speech-to-text transcription tool and a text-to-speech conversion tool. The code is\nopen-source and can be installed using pip.\n\nKey Features\n\n- [Preprocess ASR text with ease](#preprocess)\n- [Evaluate ASR output quality](#Evaluation)\n- [Transcribe speech to Hubert code](#speech-to-discrete-unit)\n- [Convert unit code to speech](#discrete-unit-to-speech)\n- [Enhance speech quality with a noise reduction tool](#speech-enhancement)\n- [LiveASR tool for real-time speech recognition](#liveasr---huggingfaces-model)\n- [Speaker Embedding Extraction (x-vector/d-vector)](#speaker-embedding-extraction---x-vector)\n\n## install\n\n`pip install asrp`\n\n## Preprocess\n\nASRP offers an easy-to-use set of functions to preprocess ASR text data. \nThe input data is a dictionary with the key 'sentence', and the output is the preprocessed text. \nYou can either use the fun_en function or use dynamic loading. Here's how to use it:\n\n```python\nimport asrp\n\nbatch_data = {\n 'sentence': \"I'm fine, thanks.\"\n}\nasrp.fun_en(batch_data)\n```\n\ndynamic loading\n\n```python\nimport asrp\n\nbatch_data = {\n 'sentence': \"I'm fine, thanks.\"\n}\npreprocessor = getattr(asrp, 'fun_en')\npreprocessor(batch_data)\n```\n\n## Evaluation\n\nASRP provides functions to evaluate the output quality of ASR systems using \nthe Word Error Rate (WER) and Character Error Rate (CER) metrics. \nHere's how to use it:\n\n```python\nimport asrp\n\ntargets = ['HuggingFace is great!', 'Love Transformers!', 'Let\\'s wav2vec!']\npreds = ['HuggingFace is awesome!', 'Transformers is powerful.', 'Let\\'s finetune wav2vec!']\nprint(\"chunk size WER: {:2f}\".format(100 * asrp.chunked_wer(targets, preds, chunk_size=None)))\nprint(\"chunk size CER: {:2f}\".format(100 * asrp.chunked_cer(targets, preds, chunk_size=None)))\n```\n\n## Speech to Discrete Unit\n\n```python\nimport asrp\nimport nlp2\n\n# https://github.com/facebookresearch/fairseq/blob/ust/examples/speech_to_speech/docs/textless_s2st_real_data.md\n# https://github.com/facebookresearch/fairseq/tree/main/examples/textless_nlp/gslm/ulm\nnlp2.download_file(\n 'https://huggingface.co/voidful/mhubert-base/resolve/main/mhubert_base_vp_en_es_fr_it3_L11_km1000.bin', './')\nhc = asrp.HubertCode(\"voidful/mhubert-base\", './mhubert_base_vp_en_es_fr_it3_L11_km1000.bin', 11,\n chunk_sec=30,\n worker=20)\nhc('voice file path')\n```\n\n## Discrete Unit to speech\n\n```python\nimport asrp\n\ncode = [] # discrete unit\n# https://github.com/pytorch/fairseq/tree/main/examples/textless_nlp/gslm/unit2speech\n# https://github.com/facebookresearch/fairseq/blob/ust/examples/speech_to_speech/docs/textless_s2st_real_data.md\ncs = asrp.Code2Speech(tts_checkpoint='./tts_checkpoint_best.pt', waveglow_checkpint='waveglow_256channels_new.pt')\ncs(code)\n\n# play on notebook\nimport IPython.display as ipd\n\nipd.Audio(data=cs(code), autoplay=False, rate=cs.sample_rate)\n```\n\nmhubert English hifigan vocoder example\n\n```python\nimport asrp\nimport nlp2\nimport IPython.display as ipd\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\nnlp2.download_file(\n 'https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj/g_00500000',\n './')\n\n\ntokenizer = AutoTokenizer.from_pretrained(\"voidful/mhubert-unit-tts\")\nmodel = AutoModelForSeq2SeqLM.from_pretrained(\"voidful/mhubert-unit-tts\")\nmodel.eval()\ncs = asrp.Code2Speech(tts_checkpoint='./g_00500000', vocoder='hifigan')\n\ninputs = tokenizer([\"The quick brown fox jumps over the lazy dog.\"], return_tensors=\"pt\")\ncode = tokenizer.batch_decode(model.generate(**inputs,max_length=1024))[0]\ncode = [int(i) for i in code.replace(\"</s>\",\"\").replace(\"<s>\",\"\").split(\"v_tok_\")[1:]]\nprint(code)\nipd.Audio(data=cs(code), autoplay=False, rate=cs.sample_rate)\n\n```\n\n## Speech Enhancement\n\nASRP also provides a tool to enhance speech quality with a noise reduction tool. \nfrom https://github.com/facebookresearch/fairseq/tree/main/examples/speech_synthesis/preprocessing/denoiser\n\n```python\nfrom asrp import SpeechEnhancer\n\nase = SpeechEnhancer()\nprint(ase('./test/xxx.wav'))\n```\n\n## LiveASR - huggingface's model\n\n* modify from https://github.com/oliverguhr/wav2vec2-live\n\n```python\nfrom asrp.live import LiveSpeech\n\nenglish_model = \"voidful/wav2vec2-xlsr-multilingual-56\"\nasr = LiveSpeech(english_model, device_name=\"default\")\nasr.start()\n\ntry:\n while True:\n text, sample_length, inference_time = asr.get_last_text()\n print(f\"{sample_length:.3f}s\"\n + f\"\\t{inference_time:.3f}s\"\n + f\"\\t{text}\")\n\nexcept KeyboardInterrupt:\n asr.stop()\n```\n\n## LiveASR - whisper's model\n\n```python\nfrom asrp.live import LiveSpeech\n\nwhisper_model = \"tiny\"\nasr = LiveSpeech(whisper_model, vad_mode=2, language='zh')\nasr.start()\nlast_text = \"\"\nwhile True:\n asr_text = \"\"\n try:\n asr_text, sample_length, inference_time = asr.get_last_text()\n if len(asr_text) > 0:\n print(asr_text, sample_length, inference_time)\n except KeyboardInterrupt:\n asr.stop()\n break\n\n```\n\n## Speaker Embedding Extraction - x vector\n\nfrom https://speechbrain.readthedocs.io/en/latest/API/speechbrain.lobes.models.Xvector.html\n\n```python\nfrom asrp.speaker_embedding import extract_x_vector\n\nextract_x_vector('./test/xxx.wav')\n```\n\n## Speaker Embedding Extraction - d vector\n\nfrom https://github.com/yistLin/dvector\n\n```python\nfrom asrp.speaker_embedding import extract_d_vector\n\nextract_d_vector('./test/xxx.wav')\n```\n",
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