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import gradio as gr
import torch
import time
import librosa
import soundfile
import nemo.collections.asr as nemo_asr
import tempfile
import os
import uuid
SAMPLE_RATE = 16000
model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_en_conformer_transducer_xlarge")
model.change_decoding_strategy(None)
model.eval()
def process_audio_file(file):
data, sr = librosa.load(file)
if sr != SAMPLE_RATE:
data = librosa.resample(data, orig_sr=sr, target_sr=SAMPLE_RATE)
# monochannel
data = librosa.to_mono(data)
return data
def transcribe(audio, state=""):
# Grant additional context
# time.sleep(1)
if state is None:
state = ""
audio_data = process_audio_file(audio)
with tempfile.TemporaryDirectory() as tmpdir:
# Filepath transcribe
audio_path = os.path.join(tmpdir, f'audio_{uuid.uuid4()}.wav')
soundfile.write(audio_path, audio_data, SAMPLE_RATE)
transcriptions = model.transcribe([audio_path])
# Direct transcribe
# transcriptions = model.transcribe([audio])
# if transcriptions form a tuple (from RNNT), extract just "best" hypothesis
if type(transcriptions) == tuple and len(transcriptions) == 2:
transcriptions = transcriptions[0]
transcriptions = transcriptions[0]
state = state + transcriptions + " "
return state, state
iface = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(source="microphone", type='filepath', streaming=True),
"state",
],
outputs=[
"textbox",
"state",
],
layout="horizontal",
theme="huggingface",
title="NeMo Streaming Conformer Transducer Large - English",
description="Demo for English speech recognition using Conformer Transducers",
allow_flagging='never',
live=True,
)
iface.launch()
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