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from transformers import pipeline
import tempfile
import gradio as gr
from neon_tts_plugin_coqui import CoquiTTS
import os
import time
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from flores200_codes import flores_codes
pipe = pipeline(model="Yuyang2022/yue") # change to "your-username/the-name-you-picked"
LANGUAGES = list(CoquiTTS.langs.keys())
coquiTTS = CoquiTTS()
def audio_tts(audio, language:str, lang):
text = pipe(audio)["text"]
text = translation("zho_Hant", lang, text)
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
coquiTTS.get_tts(text, fp, speaker = {"language" : language})
return fp.name
def load_models():
# build model and tokenizer
model_name_dict = {
"nllb-distilled-600M": "facebook/nllb-200-distilled-600M",
}
model_dict = {}
for call_name, real_name in model_name_dict.items():
print("\tLoading model: %s" % call_name)
model = AutoModelForSeq2SeqLM.from_pretrained(real_name)
tokenizer = AutoTokenizer.from_pretrained(real_name)
model_dict[call_name + "_model"] = model
model_dict[call_name + "_tokenizer"] = tokenizer
return model_dict
def translation(source, target, text):
if len(model_dict) == 2:
model_name = "nllb-distilled-600M"
start_time = time.time()
source = "zho_Hant" #flores_codes[source]
target = flores_codes[target]
model = model_dict[model_name + "_model"]
tokenizer = model_dict[model_name + "_tokenizer"]
translator = pipeline(
"translation",
model=model,
tokenizer=tokenizer,
src_lang=source,
tgt_lang=target,
)
output = translator(text, max_length=400)
end_time = time.time()
output = output[0]["translation_text"]
result = {
"inference_time": end_time - start_time,
"source": source,
"target": target,
"result": output,
}
return output
if __name__ == "__main__":
print("\tinit models")
global model_dict
model_dict = load_models()
lang_codes = list(flores_codes.keys())
# define gradio demo
inputs = [gr.Audio(source="microphone", type="filepath"),
gr.Radio(
label="Target text Language",
choices=LANGUAGES, value="en"),
gr.inputs.Dropdown(lang_codes, default="English", label="Target text Language"),]
outputs = gr.Audio(label="Output")
demo = gr.Interface(fn=audio_tts, inputs=inputs, outputs=outputs,
title="translation - speech to speech",
description="Realtime demo for speech translation.",)
demo.launch() |