Update app.py

app.py

@@ -1,5 +1,13 @@
 import gradio as gr
-from subprocess import Popen
+
+import argparse
+import soundfile as sf
+import tempfile
+from pathlib import Path
+import os
+import subprocess
+import sys
+import re
 
 # from transformers import AutoProcessor, AutoModelForPreTraining
 
@@ -7,10 +15,36 @@ from subprocess import Popen
 
 # model = AutoModelForPreTraining.from_pretrained("patrickvonplaten/mms-1b")
 
-model = "base_300m.pt"
+def process(audio, model, lang, format):    
+    with tempfile.TemporaryDirectory() as tmpdir:
+        print(">>> preparing tmp manifest dir ...", file=sys.stderr)
+        tmpdir = Path(tmpdir)
+        with open(tmpdir / "dev.tsv", "w") as fw:
+            fw.write("/\n")
+            for audio in audio:
+                nsample = sf.SoundFile(audio).frames
+                fw.write(f"{audio}\t{nsample}\n")
+        with open(tmpdir / "dev.uid", "w") as fw:
+            fw.write(f"{audio}\n"*len(audio))
+        with open(tmpdir / "dev.ltr", "w") as fw:
+            fw.write("d u m m y | d u m m y\n"*len(audio))
+        with open(tmpdir / "dev.wrd", "w") as fw:
+            fw.write("dummy dummy\n"*len(audio))
+        cmd = f"""
+        PYTHONPATH=. PREFIX=INFER HYDRA_FULL_ERROR=1 python infer.py -m decoding.type=viterbi dataset.max_tokens=4000000 distributed_training.distributed_world_size=1 "common_eval.path='{model}'" task.data={tmpdir} dataset.gen_subset="{lang}:dev" common_eval.post_process={format} decoding.results_path={tmpdir}
+        """
+        print(">>> loading model & running inference ...", file=sys.stderr)
+        subprocess.run(cmd, shell=True, stdout=subprocess.DEVNULL,)
+        with open(tmpdir/"hypo.word") as fr:
+            for ii, hypo in enumerate(fr):
+                hypo = re.sub("\(\S+\)$", "", hypo).strip()
+                print(f'===============\nInput: {audio[ii]}\nOutput: {hypo}')
 
 def transcribe(audio):
-    Popen('python mms_infer.py --model model --lang eng --audio audio')
+    model = "base_300m.pt"
+    lang = "eng"
+    format = "letter"
+    process(audio, model, lang, format)
 
 gr.Interface(
     title = 'MetaAI (Facebook Research) MMS (Massively Multilingual Speech) ASR',