Spaces:
Runtime error
Runtime error
File size: 4,914 Bytes
e954652 1f705ff e954652 1f705ff e954652 1f705ff e954652 1f705ff e954652 1f705ff e954652 1f705ff e954652 1f705ff |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 |
import streamlit as st
import firebase_admin
from firebase_admin import credentials
from firebase_admin import firestore
import datetime
from transformers import pipeline
import gradio as gr
import tempfile
from typing import Optional
import numpy as np
from TTS.utils.manage import ModelManager
from TTS.utils.synthesizer import Synthesizer
@st.experimental_singleton
def get_db_firestore():
cred = credentials.Certificate('test.json')
firebase_admin.initialize_app(cred, {'projectId': u'clinical-nlp-b9117',})
db = firestore.client()
return db
db = get_db_firestore()
asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
MODEL_NAMES = [
# "en/ek1/tacotron2",
"en/ljspeech/tacotron2-DDC",
# "en/ljspeech/tacotron2-DDC_ph",
# "en/ljspeech/glow-tts",
# "en/ljspeech/tacotron2-DCA",
# "en/ljspeech/speedy-speech-wn",
# "en/ljspeech/vits",
# "en/vctk/sc-glow-tts",
# "en/vctk/vits",
# "en/sam/tacotron-DDC",
# "es/mai/tacotron2-DDC",
"fr/mai/tacotron2-DDC",
"zh-CN/baker/tacotron2-DDC-GST",
"nl/mai/tacotron2-DDC",
"de/thorsten/tacotron2-DCA",
# "ja/kokoro/tacotron2-DDC",
]
MODELS = {}
manager = ModelManager()
for MODEL_NAME in MODEL_NAMES:
print(f"downloading {MODEL_NAME}")
model_path, config_path, model_item = manager.download_model(f"tts_models/{MODEL_NAME}")
vocoder_name: Optional[str] = model_item["default_vocoder"]
vocoder_path = None
vocoder_config_path = None
if vocoder_name is not None:
vocoder_path, vocoder_config_path, _ = manager.download_model(vocoder_name)
synthesizer = Synthesizer(
model_path, config_path, None, vocoder_path, vocoder_config_path,
)
MODELS[MODEL_NAME] = synthesizer
def transcribe(audio):
text = asr(audio)["text"]
return text
classifier = pipeline("text-classification")
def speech_to_text(speech):
text = asr(speech)["text"]
return text
def text_to_sentiment(text):
sentiment = classifier(text)[0]["label"]
return sentiment
def upsert(text):
date_time =str(datetime.datetime.today())
doc_ref = db.collection('Text2SpeechSentimentSave').document(date_time)
doc_ref.set({u'firefield': 'Recognize Speech', u'first': 'https://huggingface.co/spaces/awacke1/Text2SpeechSentimentSave', u'last': text, u'born': date_time,})
saved = select('Text2SpeechSentimentSave', date_time)
# check it here: https://console.firebase.google.com/u/0/project/clinical-nlp-b9117/firestore/data/~2FStreamlitSpaces
return saved
def select(collection, document):
doc_ref = db.collection(collection).document(document)
doc = doc_ref.get()
docid = ("The id is: ", doc.id)
contents = ("The contents are: ", doc.to_dict())
return contents
def selectall(text):
docs = db.collection('Text2SpeechSentimentSave').stream()
doclist=''
for doc in docs:
#docid=doc.id
#dict=doc.to_dict()
#doclist+=doc.to_dict()
r=(f'{doc.id} => {doc.to_dict()}')
doclist += r
return doclist
def tts(text: str, model_name: str):
print(text, model_name)
synthesizer = MODELS.get(model_name, None)
if synthesizer is None:
raise NameError("model not found")
wavs = synthesizer.tts(text)
# output = (synthesizer.output_sample_rate, np.array(wavs))
# return output
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
synthesizer.save_wav(wavs, fp)
return fp.name
demo = gr.Blocks()
with demo:
#audio_file = gr.Audio(type="filepath")
audio_file = gr.inputs.Audio(source="microphone", type="filepath")
text = gr.Textbox()
label = gr.Label()
saved = gr.Textbox()
savedAll = gr.Textbox()
TTSchoice = gr.inputs.Radio( label="Pick a TTS Model", choices=MODEL_NAMES, )
b1 = gr.Button("Recognize Speech")
b2 = gr.Button("Classify Sentiment")
b3 = gr.Button("Save Speech to Text")
b4 = gr.Button("Retrieve All")
b5 = gr.Button("Read It Back Aloud")
b1.click(speech_to_text, inputs=audio_file, outputs=text)
b2.click(text_to_sentiment, inputs=text, outputs=label)
b3.click(upsert, inputs=text, outputs=saved)
b4.click(selectall, inputs=text, outputs=savedAll)
b5.click(tts, inputs=text,TTSchoice, outputs=Audio(label="Output"))
demo.launch(share=True)
#iface = gr.Interface(
# fn=tts,
# inputs=[
# gr.inputs.Textbox( label="Input", default="Hello, how are you?", ),
# gr.inputs.Radio( label="Pick a TTS Model", choices=MODEL_NAMES, ),
# ],
# outputs=gr.outputs.Audio(label="Output"),
# title="🐸💬 - Coqui TTS",
# theme="huggingface",
# description="🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production",
# article="more info at https://github.com/coqui-ai/TTS",
#)
#iface.launch() |