voicebot_german / utils.py
remzicam's picture
Upload 3 files
86e3069
raw
history blame
4.1 kB
"""Some utility functions for the app."""
from base64 import b64encode
from io import BytesIO
from gtts import gTTS
from mtranslate import translate
from speech_recognition import AudioFile, Recognizer
from transformers import (BlenderbotSmallForConditionalGeneration,
BlenderbotSmallTokenizer)
def stt(audio: object, language: str) -> str:
"""Converts speech to text.
Args:
audio: record of user speech
Returns:
text (str): recognized speech of user
"""
r = Recognizer()
# open the audio file
with AudioFile(audio) as source:
# listen for the data (load audio to memory)
audio_data = r.record(source)
# recognize (convert from speech to text)
text = r.recognize_google(audio_data, language=language)
return text
def to_en_translation(text: str, language: str) -> str:
"""Translates text from specified language to English.
Args:
text (str): input text
language (str): desired language
Returns:
str: translated text
"""
return translate(text, "en", language)
def from_en_translation(text: str, language: str) -> str:
"""Translates text from english to specified language.
Args:
text (str): input text
language (str): desired language
Returns:
str: translated text
"""
return translate(text, language, "en")
class TextGenerationPipeline:
"""Pipeline for text generation of blenderbot model.
Returns:
str: generated text
"""
# load tokenizer and the model
model_name = "facebook/blenderbot_small-90M"
tokenizer = BlenderbotSmallTokenizer.from_pretrained(model_name)
model = BlenderbotSmallForConditionalGeneration.from_pretrained(model_name)
def __init__(self, **kwargs):
"""Specififying text generation parameters.
For example: max_length=100 which generates text shorter than
100 tokens. Visit:
https://huggingface.co/docs/transformers/main_classes/text_generation
for more parameters
"""
self.__dict__.update(kwargs)
def preprocess(self, text) -> str:
"""Tokenizes input text.
Args:
text (str): user specified text
Returns:
torch.Tensor (obj): text representation as tensors
"""
return self.tokenizer(text, return_tensors="pt")
def postprocess(self, outputs) -> str:
"""Converts tensors into text.
Args:
outputs (torch.Tensor obj): model text generation output
Returns:
str: generated text
"""
return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
def __call__(self, text: str) -> str:
"""Generates text from input text.
Args:
text (str): user specified text
Returns:
str: generated text
"""
tokenized_text = self.preprocess(text)
output = self.model.generate(**tokenized_text, **self.__dict__)
return self.postprocess(output)
def tts(text: str, language: str) -> object:
"""Converts text into audio object.
Args:
text (str): generated answer of bot
Returns:
object: text to speech object
"""
return gTTS(text=text, lang=language, slow=False)
def tts_to_bytesio(tts_object: object) -> bytes:
"""Converts tts object to bytes.
Args:
tts_object (object): audio object obtained from gtts
Returns:
bytes: audio bytes
"""
bytes_object = BytesIO()
tts_object.write_to_fp(bytes_object)
bytes_object.seek(0)
return bytes_object.getvalue()
def html_audio_autoplay(bytes: bytes) -> object:
"""Creates html object for autoplaying audio at gradio app.
Args:
bytes (bytes): audio bytes
Returns:
object: html object that provides audio autoplaying
"""
b64 = b64encode(bytes).decode()
html = f"""
<audio controls autoplay>
<source src="data:audio/wav;base64,{b64}" type="audio/wav">
</audio>
"""
return html