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"""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
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