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import gradio as gr
import numpy as np
import torch
from transformers import pipeline
device = "cuda:0" if torch.cuda.is_available() else "cpu"
# load speech translation checkpoint
asr_pipe = pipeline(
"automatic-speech-recognition",
model="openai/whisper-base",
device=device,
chunk_length_s=30,
use_fast=True,
)
# load text translation checkpoint
translation_pipe = pipeline(
"translation",
model="facebook/nllb-200-distilled-600M",
use_fast=True,
device=device,
)
# load text-to-speech checkpoint and speaker embeddings
tts_pipe = pipeline(
"text-to-speech",
model="JackismyShephard/speecht5_tts-finetuned-nst-da",
use_fast=True,
device=device,
revision="5af228df418092b681cf31c31e413bdd2b5f9c8c",
)
speaker_embedding_path = "female_23_vestjylland.npy"
speaker_embedding = np.load(speaker_embedding_path)
speaker_embedding_tensor = torch.tensor(speaker_embedding).unsqueeze(0)
target_dtype = np.int16
max_range = np.iinfo(target_dtype).max
def translate(audio):
outputs = asr_pipe(
audio,
batch_size=8,
generate_kwargs={
"task": "translate",
},
)
translated_text = translation_pipe(
outputs["text"],
src_lang="eng_Latn",
tgt_lang="dan_Latn",
)[0]["translation_text"]
return translated_text
def synthesise(text):
if len(text.strip()) == 0:
return (16000, np.zeros(0))
text = replace_danish_letters(text)
forward_params = {"speaker_embeddings": speaker_embedding_tensor}
speech = tts_pipe(text, forward_params=forward_params)
sr, audio = speech["sampling_rate"], speech["audio"]
audio = (audio * max_range).astype(np.int16)
return sr, audio
def speech_to_speech_translation(audio):
translated_text = translate(audio)
return synthesise(translated_text)
def replace_danish_letters(text):
for src, dst in replacements:
text = text.replace(src, dst)
return text
replacements = [
("&", "og"),
("\r", " "),
("´", ""),
("\\", ""),
("¨", " "),
("Å", "AA"),
("Æ", "AE"),
("É", "E"),
("Ö", "OE"),
("Ø", "OE"),
("á", "a"),
("ä", "ae"),
("å", "aa"),
("è", "e"),
("î", "i"),
("ô", "oe"),
("ö", "oe"),
("ø", "oe"),
("ü", "y"),
]
title = "Speech to Danish Speech Translation"
description = """
Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in Danish. Demo uses:
1. OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech to english text translation
2. Facebook's [NLBB](https://huggingface.co/facebook/nllb-200-distilled-600M) model for english to danish text translation
3. JackismyShephard's [speecht5_tts-finetuned-nst-da](https://huggingface.co/JackismyShephard/speecht5_tts-finetuned-nst-da) model for danish speech synthesis
"""
demo = gr.Interface(
fn=speech_to_speech_translation,
inputs=gr.Audio(label="Input Speech", type="filepath"),
outputs=gr.Audio(label="Translated Speech", type="numpy"),
title=title,
description=description,
examples=[["./example.wav"]],
cache_examples=True,
allow_flagging="never",
)
demo.launch()
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