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Running
on
Zero
File size: 9,028 Bytes
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from __future__ import annotations
import os
import gradio as gr
import spaces
import torch
import torchaudio
from transformers import (
SeamlessM4TFeatureExtractor,
SeamlessM4TTokenizer,
SeamlessM4Tv2ForSpeechToText,
)
from lang_list import (
ASR_TARGET_LANGUAGE_NAMES,
LANGUAGE_NAME_TO_CODE,
S2ST_TARGET_LANGUAGE_NAMES,
S2TT_TARGET_LANGUAGE_NAMES,
T2ST_TARGET_LANGUAGE_NAMES,
TEXT_SOURCE_LANGUAGE_NAMES,
)
DESCRIPTION = """\
### **IndicSeamless: Speech-to-Text Translation Model for Indian Languages** 🎙️➡️📜
This Gradio demo showcases **IndicSeamless**, a fine-tuned **SeamlessM4T-v2-large** model for **speech-to-text translation** across **13 Indian languages and English**. Trained on **BhasaAnuvaad**, the largest open-source speech translation dataset for Indian languages, it delivers **accurate and robust translations** across diverse linguistic and acoustic conditions.
🔗 **Model Checkpoint:** [ai4bharat/indic-seamless](https://huggingface.co/ai4bharat/indic-seamless)
#### **How to Use:**
1. **Upload or record** an audio clip in any supported Indian language.
2. Click **"Translate"** to generate the corresponding text in the target language.
3. View or copy the output for further use.
🚀 Try it out and experience seamless speech translation for Indian languages!
"""
hf_token = os.getenv("HF_TOKEN")
device = "cuda:0" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
torch_dtype = torch.bfloat16 if device != "cpu" else torch.float32
model = SeamlessM4Tv2ForSpeechToText.from_pretrained("ai4bharat/indic-seamless", torch_dtype=torch_dtype, token=hf_token).to(device)
processor = SeamlessM4TFeatureExtractor.from_pretrained("ai4bharat/indic-seamless", token=hf_token)
tokenizer = SeamlessM4TTokenizer.from_pretrained("ai4bharat/indic-seamless", token=hf_token)
CACHE_EXAMPLES = os.getenv("CACHE_EXAMPLES") == "1" and torch.cuda.is_available()
AUDIO_SAMPLE_RATE = 16000
MAX_INPUT_AUDIO_LENGTH = 60 # in seconds
DEFAULT_TARGET_LANGUAGE = "Hindi"
def preprocess_audio(input_audio: str) -> None:
arr, org_sr = torchaudio.load(input_audio)
new_arr = torchaudio.functional.resample(arr, orig_freq=org_sr, new_freq=AUDIO_SAMPLE_RATE)
max_length = int(MAX_INPUT_AUDIO_LENGTH * AUDIO_SAMPLE_RATE)
if new_arr.shape[1] > max_length:
new_arr = new_arr[:, :max_length]
gr.Warning(f"Input audio is too long. Only the first {MAX_INPUT_AUDIO_LENGTH} seconds is used.")
torchaudio.save(input_audio, new_arr, sample_rate=int(AUDIO_SAMPLE_RATE))
@spaces.GPU
def run_s2tt(input_audio: str, source_language: str, target_language: str) -> str:
# preprocess_audio(input_audio)
# source_language_code = LANGUAGE_NAME_TO_CODE[source_language]
target_language_code = LANGUAGE_NAME_TO_CODE[target_language]
input_audio, orig_freq = torchaudio.load(input_audio)
input_audio = torchaudio.functional.resample(input_audio, orig_freq=orig_freq, new_freq=16000)
audio_inputs= processor(input_audio, sampling_rate=16000, return_tensors="pt").to(device=device, dtype=torch_dtype)
text_out = model.generate(**audio_inputs, tgt_lang=target_language_code)[0].float().cpu().numpy().squeeze()
return tokenizer.decode(text_out, clean_up_tokenization_spaces=True, skip_special_tokens=True)
@spaces.GPU
def run_asr(input_audio: str, target_language: str) -> str:
# preprocess_audio(input_audio)
target_language_code = LANGUAGE_NAME_TO_CODE[target_language]
input_audio, orig_freq = torchaudio.load(input_audio)
input_audio = torchaudio.functional.resample(input_audio, orig_freq=orig_freq, new_freq=16000)
audio_inputs= processor(input_audio, sampling_rate=16000, return_tensors="pt").to(device=device, dtype=torch_dtype)
text_out = model.generate(**audio_inputs, tgt_lang=target_language_code)[0].float().cpu().numpy().squeeze()
return tokenizer.decode(text_out, clean_up_tokenization_spaces=True, skip_special_tokens=True)
with gr.Blocks() as demo_s2st:
with gr.Row():
with gr.Column():
with gr.Group():
input_audio = gr.Audio(label="Input speech", type="filepath")
source_language = gr.Dropdown(
label="Source language",
choices=ASR_TARGET_LANGUAGE_NAMES,
value="English",
)
target_language = gr.Dropdown(
label="Target language",
choices=S2ST_TARGET_LANGUAGE_NAMES,
value=DEFAULT_TARGET_LANGUAGE,
)
btn = gr.Button("Translate")
with gr.Column():
with gr.Group():
output_audio = gr.Audio(
label="Translated speech",
autoplay=False,
streaming=False,
type="numpy",
)
output_text = gr.Textbox(label="Translated text")
with gr.Blocks() as demo_s2tt:
with gr.Row():
with gr.Column():
with gr.Group():
input_audio = gr.Audio(label="Input speech", type="filepath")
source_language = gr.Dropdown(
label="Source language",
choices=ASR_TARGET_LANGUAGE_NAMES,
value="English",
)
target_language = gr.Dropdown(
label="Target language",
choices=S2TT_TARGET_LANGUAGE_NAMES,
value=DEFAULT_TARGET_LANGUAGE,
)
btn = gr.Button("Translate")
with gr.Column():
output_text = gr.Textbox(label="Translated text")
gr.Examples(
examples=[
["assets/Bengali.wav", "Bengali", "English"],
["assets/Gujarati.wav", "Gujarati", "Hindi"],
["assets/Punjabi.wav", "Punjabi", "Hindi"],
],
inputs=[input_audio, source_language, target_language],
outputs=output_text,
fn=run_s2tt,
cache_examples=CACHE_EXAMPLES,
api_name=False,
)
btn.click(
fn=run_s2tt,
inputs=[input_audio, source_language, target_language],
outputs=output_text,
api_name="s2tt",
)
with gr.Blocks() as demo_t2st:
with gr.Row():
with gr.Column():
with gr.Group():
input_text = gr.Textbox(label="Input text")
with gr.Row():
source_language = gr.Dropdown(
label="Source language",
choices=TEXT_SOURCE_LANGUAGE_NAMES,
value="English",
)
target_language = gr.Dropdown(
label="Target language",
choices=T2ST_TARGET_LANGUAGE_NAMES,
value=DEFAULT_TARGET_LANGUAGE,
)
btn = gr.Button("Translate")
with gr.Column():
with gr.Group():
output_audio = gr.Audio(
label="Translated speech",
autoplay=False,
streaming=False,
type="numpy",
)
output_text = gr.Textbox(label="Translated text")
with gr.Blocks() as demo_asr:
with gr.Row():
with gr.Column():
with gr.Group():
input_audio = gr.Audio(label="Input speech", type="filepath")
target_language = gr.Dropdown(
label="Target language",
choices=ASR_TARGET_LANGUAGE_NAMES,
value=DEFAULT_TARGET_LANGUAGE,
)
btn = gr.Button("Transcribe")
with gr.Column():
output_text = gr.Textbox(label="Transcribed text")
gr.Examples(
examples=[
["assets/Bengali.wav", "Bengali", "English"],
["assets/Gujarati.wav", "Gujarati", "Hindi"],
["assets/Punjabi.wav", "Punjabi", "Hindi"],
],
inputs=[input_audio, target_language],
outputs=output_text,
fn=run_asr,
cache_examples=CACHE_EXAMPLES,
api_name=False,
)
btn.click(
fn=run_asr,
inputs=[input_audio, target_language],
outputs=output_text,
api_name="asr",
)
with gr.Blocks(css="style.css") as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(
value="Duplicate Space for private use",
elem_id="duplicate-button",
visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
)
with gr.Tabs():
# with gr.Tab(label="S2ST"):
# demo_s2st.render()
with gr.Tab(label="S2TT"):
demo_s2tt.render()
# with gr.Tab(label="T2ST"):
# demo_t2st.render()
# with gr.Tab(label="T2TT"):
# demo_t2tt.render()
with gr.Tab(label="ASR"):
demo_asr.render()
demo.launch(share=True)
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