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app.py

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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()

packages.txt

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+ffmpeg
+libsndfile1

requirements.txt

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+nemo_toolkit[asr]