diff --git a/examples/mms/README.md b/examples/mms/README.md
index 48460b375b..dfd8b85fbd 100644
--- a/examples/mms/README.md
+++ b/examples/mms/README.md
@@ -53,7 +53,8 @@ wget https://dl.fbaipublicfiles.com/mms/tts/azj-script_latin.tar.gz # North Azer
 Run this command to transcribe one or more audio files:
 ```shell command
 cd /path/to/fairseq-py/
-python examples/mms/asr/infer/mms_infer.py --model "/path/to/asr/model" --lang lang_code --audio "/path/to/audio_1.wav" "/path/to/audio_1.wav"
+python examples/mms/asr/infer/mms_infer.py --model "/path/to/asr/model" --lang lang_code \
+  --audio "/path/to/audio_1.wav" "/path/to/audio_2.wav" "/path/to/audio_3.wav"
 ```
 
 For more advance configuration and calculate CER/WER, you could prepare manifest folder by creating a folder with this format: 
diff --git a/examples/mms/asr/infer/mms_infer.py b/examples/mms/asr/infer/mms_infer.py
index f9e06cd222..65f86b3148 100644
--- a/examples/mms/asr/infer/mms_infer.py
+++ b/examples/mms/asr/infer/mms_infer.py
@@ -21,28 +21,38 @@ def parser():
     parser.add_argument("--format", type=str, choices=["none", "letter"], default="letter")
     return parser.parse_args()
 
+def reorder_decode(hypos):
+    outputs = []
+    for hypo in hypos:
+        idx = int(re.findall("\(None-(\d+)\)$", hypo)[0])
+        hypo = re.sub("\(\S+\)$", "", hypo).strip()
+        outputs.append((idx, hypo))
+    outputs = sorted(outputs)
+    return outputs
+
 def process(args):    
     with tempfile.TemporaryDirectory() as tmpdir:
         print(">>> preparing tmp manifest dir ...", file=sys.stderr)
         tmpdir = Path(tmpdir)
-        with open(tmpdir / "dev.tsv", "w") as fw:
+        with open(tmpdir / "dev.tsv", "w") as fw, open(tmpdir / "dev.uid", "w") as fu:
             fw.write("/\n")
             for audio in args.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(args.audio))
+                fu.write(f"{audio}\n")
         with open(tmpdir / "dev.ltr", "w") as fw:
-            fw.write("d u m m y | d u m m y\n"*len(args.audio))
+            fw.write("d u m m y | d u m m y |\n"*len(args.audio))
         with open(tmpdir / "dev.wrd", "w") as fw:
             fw.write("dummy dummy\n"*len(args.audio))
         cmd = f"""
-        PYTHONPATH=. PREFIX=INFER HYDRA_FULL_ERROR=1 python examples/speech_recognition/new/infer.py -m --config-dir examples/mms/asr/config/ --config-name infer_common decoding.type=viterbi dataset.max_tokens=4000000 distributed_training.distributed_world_size=1 "common_eval.path='{args.model}'" task.data={tmpdir} dataset.gen_subset="{args.lang}:dev" common_eval.post_process={args.format} decoding.results_path={tmpdir}
+        PYTHONPATH=. PREFIX=INFER HYDRA_FULL_ERROR=1 python examples/speech_recognition/new/infer.py -m --config-dir examples/mms/asr/config/ --config-name infer_common decoding.type=viterbi dataset.max_tokens=1440000 distributed_training.distributed_world_size=1 "common_eval.path='{args.model}'" task.data={tmpdir} dataset.gen_subset="{args.lang}:dev" common_eval.post_process={args.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):
+            hypos = fr.readlines()
+            outputs = reorder_decode(hypos)
+            for ii, hypo in outputs:
                 hypo = re.sub("\(\S+\)$", "", hypo).strip()
                 print(f'===============\nInput: {args.audio[ii]}\nOutput: {hypo}')