social-ear-pt-br / helpers.py
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Create helpers.py
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import os
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_io as tfio
import csv
from scipy.io import wavfile
import scipy
import librosa
import soundfile as sf
import time
import soundfile as sf
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from transformers import AutoProcessor
from transformers import BarkModel
from optimum.bettertransformer import BetterTransformer
import torch
from nemo.collections.tts.models import FastPitchModel
from nemo.collections.tts.models import HifiGanModel
from deep_translator import GoogleTranslator
from haystack.document_stores import InMemoryDocumentStore
from haystack.nodes import EmbeddingRetriever
# --- Load models ---
#Load a model from tensorflow hub
def load_model_hub(model_url):
model = hub.load(model_url)
return model
# Load a model from the project folder
def load_model_file(model_path):
interpreter = tf.lite.Interpreter(model_path)
interpreter.allocate_tensors()
return interpreter
# --- Initialize models ---
def initialize_text_to_speech_model():
spec_generator = FastPitchModel.from_pretrained("nvidia/tts_en_fastpitch")
# Load vocoder
model = HifiGanModel.from_pretrained(model_name="nvidia/tts_hifigan")
return spec_generator, model
def initialize_tt5_model():
from transformers import SpeechT5ForTextToSpeech, SpeechT5Processor, SpeechT5HifiGan
from datasets import load_dataset
dataset = load_dataset("pedropauletti/librispeech-portuguese")
model = SpeechT5ForTextToSpeech.from_pretrained("pedropauletti/speecht5_finetuned_librispeech_pt")
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
example = dataset["test"][100]
speaker_embeddings = torch.tensor(example["speaker_embeddings"]).unsqueeze(0)
return model, processor, vocoder, speaker_embeddings
def load_qa_model():
document_store = InMemoryDocumentStore()
retriever = EmbeddingRetriever(
document_store=document_store,
embedding_model="sentence-transformers/all-MiniLM-L6-v2",
use_gpu=False,
scale_score=False,
)
# Get dataframe with columns "question", "answer" and some custom metadata
df = pd.read_csv('content/social-faq.csv', on_bad_lines='skip', delimiter=';')
# Minimal cleaning
df.fillna(value="", inplace=True)
df["question"] = df["question"].apply(lambda x: x.strip())
questions = list(df["question"].values)
df["embedding"] = retriever.embed_queries(queries=questions).tolist()
df = df.rename(columns={"question": "content"})
# Convert Dataframe to list of dicts and index them in our DocumentStore
docs_to_index = df.to_dict(orient="records")
document_store.write_documents(docs_to_index)
return retriever
# --- Audio pre-processing ---
# Utility functions for loading audio files and making sure the sample rate is correct.
@tf.function
def load_wav_16k_mono(filename):
""" Load a WAV file, convert it to a float tensor, resample to 16 kHz single-channel audio. """
file_contents = tf.io.read_file(filename)
wav, sample_rate = tf.audio.decode_wav(
file_contents,
desired_channels=1)
wav = tf.squeeze(wav, axis=-1)
sample_rate = tf.cast(sample_rate, dtype=tf.int64)
wav = tfio.audio.resample(wav, rate_in=sample_rate, rate_out=16000)
return wav
def load_wav_16k_mono_librosa(filename):
""" Load a WAV file, convert it to a float tensor, resample to 16 kHz single-channel audio using librosa. """
wav, sample_rate = librosa.load(filename, sr=16000, mono=True)
return wav
def load_wav_16k_mono_soundfile(filename):
""" Load a WAV file, convert it to a float tensor, resample to 16 kHz single-channel audio using soundfile. """
wav, sample_rate = sf.read(filename, dtype='float32')
# Resample to 16 kHz if necessary
if sample_rate != 16000:
wav = librosa.resample(wav, orig_sr=sample_rate, target_sr=16000)
return wav
# --- History ---
def updateHistory():
global history
return history
def clearHistory():
global history
history = ""
return history
def clear():
return None
# --- Output Format ---
def format_dictionary(dictionary):
result = []
for key, value in dictionary.items():
percentage = int(value * 100)
result.append(f"{key}: {percentage}%")
return ', '.join(result)
def format_json(json_data):
confidence_strings = [f"{item['label']}: {round(item['confidence']*100)}%" for item in json_data['confidences']]
result_string = f"{', '.join(confidence_strings)}"
return result_string
def format_json_pt(json_data):
from unidecode import unidecode
confidence_strings = [f"{item['label']}... " for item in json_data['confidences']]
result_string = f"{', '.join(confidence_strings)}"
return unidecode(result_string)
# --- Classification ---
def load_label_mapping(csv_path):
label_mapping = {}
with open(csv_path, newline='', encoding='utf-8') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
label_mapping[int(row['index'])] = row['display_name']
return label_mapping
def predict_yamnet(interpreter, waveform, input_details, output_details, label_mapping):
# Pré-processamento da waveform para corresponder aos requisitos do modelo
input_shape = input_details[0]['shape']
input_data = np.array(waveform, dtype=np.float32)
if input_data.shape != input_shape:
# Redimensionar ou preencher a waveform para corresponder ao tamanho esperado
if input_data.shape[0] < input_shape[0]:
# Preencher a waveform com zeros
padding = np.zeros((input_shape[0] - input_data.shape[0],))
input_data = np.concatenate((input_data, padding))
elif input_data.shape[0] > input_shape[0]:
# Redimensionar a waveform
input_data = input_data[:input_shape[0]]
input_data = np.reshape(input_data, input_shape)
# Executar a inferência
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
# Obter os resultados da inferência
output_data = interpreter.get_tensor(output_details[0]['index'])
# Processar os resultados e imprimir nome da etiqueta
top_labels_indices = np.argsort(output_data[0])[::-1][:3]
results = []
for i in top_labels_indices:
label_name = label_mapping.get(i, "Unknown Label")
probability = float(output_data[0][i]) # Converter para float
results.append({'label': label_name, 'probability': str(probability)})
return results # Retornar um dicionário contendo a lista de resultados
def classify(audio, language="en-us"):
#Preprocessing audio
wav_data = load_wav_16k_mono_librosa(audio)
if(language == "pt-br"):
#Label Mapping
label_mapping = load_label_mapping('content/yamnet_class_map_ptbr.csv')
else:
label_mapping = load_label_mapping('content/yamnet_class_map.csv')
#Load Model by File
model = load_model_file('content/yamnet_classification.tflite')
input_details = model.get_input_details()
output_details = model.get_output_details()
#Classification
result = predict_yamnet(model, wav_data, input_details, output_details, label_mapping)
return result
def classify_realtime(language, audio, state):
#Preprocessing audio
wav_data = load_wav_16k_mono_librosa(audio)
if(language == "pt-br"):
#Label Mapping
label_mapping = load_label_mapping('content/yamnet_class_map_ptbr.csv')
else:
label_mapping = load_label_mapping('content/yamnet_class_map.csv')
#Load Model by File
model = load_model_file('content/yamnet_classification.tflite')
input_details = model.get_input_details()
output_details = model.get_output_details()
#Classification
result = predict_yamnet(model, wav_data, input_details, output_details, label_mapping)
state += result + " "
return result, state
# --- TTS ---
def generate_audio(spec_generator, model, input_text):
parsed = spec_generator.parse(input_text)
spectrogram = spec_generator.generate_spectrogram(tokens=parsed)
audio = model.convert_spectrogram_to_audio(spec=spectrogram)
return 22050, audio.cpu().detach().numpy().squeeze()
def generate_audio_tt5(model, processor, vocoder, speaker_embeddings, text):
inputs = processor(text=text, return_tensors="pt")
audio = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
return 16000, audio.cpu().detach().numpy().squeeze()
def TTS(json_input, language):
global spec_generator, model_nvidia, history
global model_tt5, processor, vocoder, speaker_embeddings
if language == 'en-us':
sr, generatedAudio = generate_audio(spec_generator, model_nvidia, format_json(json_input))
else:
sr, generatedAudio = generate_audio_tt5(model_tt5, processor, vocoder, speaker_embeddings, format_json_pt(json_input))
return (sr, generatedAudio)
def TTS_ASR(json_input, language):
global spec_generator, model_nvidia, history
global model_tt5, processor, vocoder, speaker_embeddings
if language == 'en-us':
sr, generatedAudio = generate_audio(spec_generator, model_nvidia, json_input['label'])
else:
sr, generatedAudio = generate_audio_tt5(model_tt5, processor, vocoder, speaker_embeddings, json_input['label'])
return (sr, generatedAudio)
def TTS_chatbot(language):
global spec_generator, model_nvidia, history
global model_tt5, processor, vocoder, speaker_embeddings
global last_answer
if language == 'en-us':
sr, generatedAudio = generate_audio(spec_generator, model_nvidia, last_answer)
else:
sr, generatedAudio = generate_audio_tt5(model_tt5, processor, vocoder, speaker_embeddings, last_answer)
return (sr, generatedAudio)
# --- ASR ---
def transcribe_speech(filepath, language):
print(filepath)
if(language == "pt-br"):
output = pipe(
filepath,
max_new_tokens=256,
generate_kwargs={
"task": "transcribe",
"language": "portuguese",
},
chunk_length_s=30,
batch_size=8,
)
else:
output = pipe_en(
filepath,
max_new_tokens=256,
generate_kwargs={
"task": "transcribe",
"language": "english",
},
chunk_length_s=30,
batch_size=8,
)
return output["text"]
def transcribe_speech_realtime(filepath, state):
output = pipe(
filepath,
max_new_tokens=256,
generate_kwargs={
"task": "transcribe",
"language": "english",
},
chunk_length_s=30,
batch_size=8,
)
state += output["text"] + " "
return output["text"], state
def transcribe_realtime(new_chunk, stream):
sr, y = new_chunk
y = y.astype(np.float32)
y /= np.max(np.abs(y))
if stream is not None:
stream = np.concatenate([stream, y])
else:
stream = y
return stream, pipe_en({"sampling_rate": sr, "raw": stream})["text"]
# --- Translation ---
def translate_enpt(text):
global enpt_pipeline
translation = enpt_pipeline(f"translate English to Portuguese: {text}")
return translation[0]['generated_text']
# --- Gradio Interface ---
def interface(language, audio):
global classificationResult
result = classify(language, audio)
dic = {result[0]['label']: float(result[0]['probability']),
result[1]['label']: float(result[1]['probability']),
result[2]['label']: float(result[2]['probability'])
}
# history += result[0]['label'] + '\n'
classificationResult = dic
return dic
def interface_realtime(language, audio):
global history
result = classify(language, audio)
dic = {result[0]['label']: float(result[0]['probability']),
result[1]['label']: float(result[1]['probability']),
result[2]['label']: float(result[2]['probability'])
}
history = result[0]['label'] + '\n' + history
return dic
# --- QA Model ---
def get_answers(retriever, query):
from haystack.pipelines import FAQPipeline
pipe = FAQPipeline(retriever=retriever)
from haystack.utils import print_answers
# Run any question and change top_k to see more or less answers
prediction = pipe.run(query=query, params={"Retriever": {"top_k": 1}})
answers = prediction['answers']
if answers:
return answers[0].answer
else:
return "I don't have an answer to that question"
def add_text(chat_history, text):
chat_history = chat_history + [(text, None)]
return chat_history, gr.Textbox(value="", interactive=False)
def chatbot_response(chat_history, language):
chat_history[-1][1] = ""
global retriever
global last_answer
if language == 'pt-br':
response = get_answers(retriever, GoogleTranslator(source='pt', target='en').translate(chat_history[-1][0]))
response = GoogleTranslator(source='en', target='pt').translate(response)
else:
response = get_answers(retriever, chat_history[-1][0])
last_answer = response
for character in response:
chat_history[-1][1] += character
time.sleep(0.01)
yield chat_history
retriever = load_qa_model()
spec_generator, model_nvidia = initialize_text_to_speech_model()
model_tt5, processor, vocoder, speaker_embeddings = initialize_tt5_model()
pipe = pipeline("automatic-speech-recognition", model="pedropauletti/whisper-small-pt")
pipe_en = pipeline("automatic-speech-recognition", model="openai/whisper-small")