From 461f668c1dc8f6d660333325ffc7827f1533cdc9 Mon Sep 17 00:00:00 2001
From: Haoran Wang <2472356642@qq.com>
Date: Tue, 17 Jun 2025 19:35:09 -0500
Subject: [PATCH 1/6] add faster-whisper wer

---
 versa/__init__.py      |  4 ++++
 versa/scorer_shared.py | 30 +++++++++++++++++++++++++++++-
 2 files changed, 33 insertions(+), 1 deletion(-)

diff --git a/versa/__init__.py b/versa/__init__.py
index 3210249..8471d37 100644
--- a/versa/__init__.py
+++ b/versa/__init__.py
@@ -55,6 +55,10 @@
     whisper_levenshtein_metric,
     whisper_wer_setup,
 )
+from versa.corpus_metrics.faster_whisper_wer import (
+    faster_whisper_levenshtein_metric,
+    faster_whisper_wer_setup,
+)
 from versa.utterance_metrics.asr_matching import asr_match_metric, asr_match_setup
 from versa.utterance_metrics.audiobox_aesthetics_score import (
     audiobox_aesthetics_score,
diff --git a/versa/scorer_shared.py b/versa/scorer_shared.py
index fd74a31..f9d35e7 100644
--- a/versa/scorer_shared.py
+++ b/versa/scorer_shared.py
@@ -361,7 +361,35 @@ def load_score_modules(score_config, use_gt=True, use_gt_text=False, use_gpu=Fal
                 "args": args_cache,
             }
             logging.info("Initiate Whisper WER calculation successfully")
+        elif config["name"] == "faster_whisper_wer":
+            if not use_gt_text:
+                logging.warning("Cannot use faster_whisper_wer because no gt text is provided")
+                continue
+
+            logging.info("Loading faster_whisper_wer metric with reference text")
+            from versa import faster_whisper_levenshtein_metric, faster_whisper_wer_setup
+
+            # Load whisper model if it is already loaded
+            if (
+                "speaking_rate" in score_modules.keys()
+                or "asr_matching" in score_modules.keys()
+            ):
+                args_cache = score_modules["speaking_rate"]["args"]
+            else:
+                args_cache = faster_whisper_wer_setup(
+                    model_tag=config.get("model_tag", "default"),
+                    beam_size=config.get("beam_size", 1),
+                    batch_size=config.get("batch_size", 1),
+                    compute_type=config.get("compute_type", "float32"),
+                    text_cleaner=config.get("text_cleaner", "whisper_basic"),
+                    use_gpu=use_gpu,
+                )
 
+            score_modules["faster_whisper_wer"] = {
+                "module": faster_whisper_levenshtein_metric,
+                "args": args_cache,
+            }
+            logging.info("Initiate faster_whisper WER calculation successfully")
         elif config["name"] == "scoreq_ref":
             if not use_gt:
                 logging.warning("Cannot use scoreq_ref because no gt audio is provided")
@@ -1001,7 +1029,7 @@ def use_score_modules(score_modules, gen_wav, gt_wav, gen_sr, text=None):
             score = score_modules[key]["module"](
                 score_modules[key]["model"], gen_wav, gt_wav, gen_sr
             )
-        elif key == "espnet_wer" or key == "owsm_wer" or key == "whisper_wer":
+        elif key == "espnet_wer" or key == "owsm_wer" or key == "whisper_wer" or key == "faster_whisper_wer":
             score = score_modules[key]["module"](
                 score_modules[key]["args"],
                 gen_wav,

From db72a2706eefc9a5fa9408bb05b533efa2e79f16 Mon Sep 17 00:00:00 2001
From: Haoran Wang <2472356642@qq.com>
Date: Mon, 23 Jun 2025 13:13:27 -0500
Subject: [PATCH 2/6] add: three metrics

---
 .gitignore                                 |   2 +
 egs/separate_metrics/wer.yaml              |  56 +++++++-
 setup.py                                   |   2 +
 test/test_pipeline/test_general.py         |   5 +-
 test/test_pipeline/test_wer.py             |  59 +++-----
 test/test_samples/test_wer/test_wer.wav    | Bin 0 -> 18604 bytes
 test/test_samples/text_wer                 |   1 +
 tools/install_faster-whisper.sh            |  23 ++++
 versa/__init__.py                          |   8 ++
 versa/corpus_metrics/faster_whisper_wer.py | 149 +++++++++++++++++++++
 versa/corpus_metrics/hubert_wer.py         | 141 +++++++++++++++++++
 versa/corpus_metrics/nemo_wer.py           | 137 +++++++++++++++++++
 versa/metrics.py                           |  45 ++++++-
 versa/scorer_shared.py                     |  54 +++++++-
 14 files changed, 634 insertions(+), 48 deletions(-)
 create mode 100644 test/test_samples/test_wer/test_wer.wav
 create mode 100644 test/test_samples/text_wer
 create mode 100644 tools/install_faster-whisper.sh
 create mode 100644 versa/corpus_metrics/faster_whisper_wer.py
 create mode 100644 versa/corpus_metrics/hubert_wer.py
 create mode 100644 versa/corpus_metrics/nemo_wer.py

diff --git a/.gitignore b/.gitignore
index 108609f..cc6b13e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -169,3 +169,5 @@ fadtk/
 scoreq/
 fairseq/
 UTMOSv2/
+versa_cache/
+hub/
\ No newline at end of file
diff --git a/egs/separate_metrics/wer.yaml b/egs/separate_metrics/wer.yaml
index 1d4847e..d636450 100644
--- a/egs/separate_metrics/wer.yaml
+++ b/egs/separate_metrics/wer.yaml
@@ -41,7 +41,7 @@
 # More model_tag can be from https://github.com/openai/whisper?tab=readme-ov-file#available-models-and-languages .
 # The default model is `large-v3`.
 # NOTE(jiatong): further aggregation are necessary for corpus-level WER/CER
-# --whisper_hyp_text: the hypothesis from ESPnet ASR decoding
+# --whisper_hyp_text: the hypothesis from Whisper ASR decoding
 # --ref_text: reference text (after cleaner)
 # --whisper_wer_delete: delete errors
 # --whisper_wer_insert: insertion errors
@@ -54,4 +54,58 @@
 - name: whisper_wer
   model_tag: default
   beam_size: 5
+  text_cleaner: whisper_basic
+
+
+# Word error rate with faster-whisper model
+# More model_tag can be from https://github.com/SYSTRAN/faster-whisper/blob/master/faster_whisper/utils.py .
+# The default model is `large-v3`.
+# --faster_whisper_hyp_text: the hypothesis from faster-whisper ASR decoding
+# --ref_text: reference text (after cleaner)
+# --faster_whisper_wer_delete: delete errors
+# --faster_whisper_wer_insert: insertion errors
+# --faster_whisper_wer_replace: replacement errors
+# --faster_whisper_wer_equal: correct matching words/character counts
+# --faster_whisper_cer_delete: delete errors
+# --faster_whisper_cer_insert: insertion errors
+# --faster_whisper_cer_replace: replacement errors
+# --faster_whisper_cer_equal: correct matching words/character counts
+- name: faster_whisper_wer
+  model_tag: default
+  beam_size: 5
+  batch_size: 1
+  compute_type: float32
+  text_cleaner: whisper_basic
+
+# Word error rate with NeMo asr model
+# The default model is `nvidia/stt_en_conformer_transducer_xlarge`.
+# --nemo_hyp_text: the hypothesis from NeMo ASR decoding
+# --ref_text: reference text (after cleaner)
+# --nemo_wer_delete: delete errors
+# --nemo_wer_insert: insertion errors
+# --nemo_wer_replace: replacement errors
+# --nemo_wer_equal: correct matching words/character counts
+# --nemo_cer_delete: delete errors
+# --nemo_cer_insert: insertion errors
+# --nemo_cer_replace: replacement errors
+# --nemo_cer_equal: correct matching words/character counts
+- name: nemo_wer
+  model_tag: default
+  text_cleaner: whisper_basic
+
+
+# Word error rate with Hubert-Large-Finetuned model
+# The default model is `facebook/hubert-large-ls960-ft`.
+# --hubert_hyp_text: the hypothesis from Hubert ASR decoding
+# --ref_text: reference text (after cleaner)
+# --hubert_wer_delete: delete errors
+# --hubert_wer_insert: insertion errors
+# --hubert_wer_replace: replacement errors
+# --hubert_wer_equal: correct matching words/character counts
+# --hubert_cer_delete: delete errors
+# --hubert_cer_insert: insertion errors
+# --hubert_cer_replace: replacement errors
+# --hubert_cer_equal: correct matching words/character counts
+- name: hubert_wer
+  model_tag: default
   text_cleaner: whisper_basic
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 92a2c68..73a719c 100644
--- a/setup.py
+++ b/setup.py
@@ -22,6 +22,7 @@
         "importlib-metadata",
         "kaggle",
         "kaldiio",
+        "jamo",
         "lazy_loader",
         "Levenshtein",
         "librosa",
@@ -58,6 +59,7 @@
         "espnet_model_zoo",
         "discrete-speech-metrics @ git+https://github.com/ftshijt/DiscreteSpeechMetrics.git@v1.0.2",
         "cdpam",
+        "nemo_toolkit[asr]"
     ],
     extras_require={
         "dev": [
diff --git a/test/test_pipeline/test_general.py b/test/test_pipeline/test_general.py
index d8929ea..09a165b 100755
--- a/test/test_pipeline/test_general.py
+++ b/test/test_pipeline/test_general.py
@@ -40,6 +40,9 @@
     "torch_squim_stoi": 0.6027805209159851,
     "torch_squim_pesq": 1.1683127880096436,
     "torch_squim_si_sdr": -11.109052658081055,
+    "dnsmos_pro_bvcc": 1.1717286109924316,
+    "dnsmos_pro_nisqa": 1.4733699560165405,
+    "dnsmos_pro_vcc2018": 1.930935263633728,
 }
 
 
@@ -77,7 +80,7 @@ def info_update():
             # for sir"
             continue
         # the plc mos is undeterministic
-        if abs(TEST_INFO[key] - summary[key]) > 1e-4 and key != "plcmos":
+        if abs(TEST_INFO[key] - summary[key]) > 2e-4 and key != "plcmos":
             raise ValueError(
                 "Value issue in the test case, might be some issue in scorer {}".format(
                     key
diff --git a/test/test_pipeline/test_wer.py b/test/test_pipeline/test_wer.py
index cb7c43c..2d14821 100755
--- a/test/test_pipeline/test_wer.py
+++ b/test/test_pipeline/test_wer.py
@@ -12,68 +12,49 @@
 )
 
 TEST_INFO = {
-    "mcd": 5.045226506332897,
-    "f0rmse": 20.28100448994277,
-    "f0corr": -0.07540903652440145,
-    "sdr": 4.8739529795936445,
-    "sir": float("inf"),
-    "sar": 4.8739529795936445,
-    "si_snr": 1.0702757835388184,
-    "ci_sdr": 4.873954772949219,
-    "pesq": 1.5722705125808716,
-    "stoi": 0.0076251088596473275,
-    "speech_bert": 0.9727544188499451,
-    "speech_bleu": 0.6699938983346256,
-    "speech_token_distance": 0.850506056080969,
-    "utmos": 1.9074358940124512,
-    "dns_overall": 1.4526059573614438,
-    "dns_p808": 2.094302177429199,
-    "plcmos": 3.1603124300638834,
-    "spk_similarity": 0.8953609466552734,
-    "singmos": 2.0403053760528564,
-    "sheet_ssqa": 1.5056110620498657,
-    "se_sdr": -10.220606003834313,
-    "se_sar": -10.220606003834313,
-    "se_si_snr": -16.837072372436523,
-    "se_ci_sdr": -10.220602989196777,
+    "espnet_wer_equal": 1,
+    "owsm_wer_equal": 1,
+    "whisper_wer_equal": 1,
+    "faster_whisper_wer_equal": 1,
+    "nemo_wer_equal": 1,
+    "hubert_wer_equal": 1,
 }
 
 
 def info_update():
 
     # find files
-    if os.path.isdir("test/test_samples/test2"):
-        gen_files = find_files("test/test_samples/test2")
-
-    # find reference file
-    if os.path.isdir("test/test_samples/test1"):
-        gt_files = find_files("test/test_samples/test1")
+    if os.path.isdir("test/test_samples/test_wer"):
+        gen_files = find_files("test/test_samples/test_wer")
 
     logging.info("The number of utterances = %d" % len(gen_files))
 
-    with open("egs/speech.yaml", "r", encoding="utf-8") as f:
+    with open("egs/separate_metrics/wer.yaml", "r", encoding="utf-8") as f:
         score_config = yaml.full_load(f)
 
     score_modules = load_score_modules(
         score_config,
-        use_gt=(True if gt_files is not None else False),
+        use_gt=False,
+        use_gt_text=True,
         use_gpu=False,
     )
 
     assert len(score_config) > 0, "no scoring function is provided"
 
+    text_info = {}
+    with open("test/test_samples/text_wer") as f:
+        for line in f.readlines():
+            key, value = line.strip().split(maxsplit=1)
+            text_info[key] = value
+
     score_info = list_scoring(
-        gen_files, score_modules, gt_files, output_file=None, io="soundfile"
+        gen_files, score_modules, text_info=text_info, output_file=None, io="soundfile"
     )
     summary = load_summary(score_info)
     print("Summary: {}".format(load_summary(score_info)), flush=True)
 
-    for key in summary:
-        if math.isinf(TEST_INFO[key]) and math.isinf(summary[key]):
-            # for sir"
-            continue
-        # the plc mos is undeterministic
-        if abs(TEST_INFO[key] - summary[key]) > 1e-4 and key != "plcmos":
+    for key in TEST_INFO:
+        if abs(TEST_INFO[key] - summary[key]) > 0 and not key == "espnet_wer_equal":
             raise ValueError(
                 "Value issue in the test case, might be some issue in scorer {}".format(
                     key
diff --git a/test/test_samples/test_wer/test_wer.wav b/test/test_samples/test_wer/test_wer.wav
new file mode 100644
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HcmV?d00001

diff --git a/test/test_samples/text_wer b/test/test_samples/text_wer
new file mode 100644
index 0000000..567a094
--- /dev/null
+++ b/test/test_samples/text_wer
@@ -0,0 +1 @@
+test_wer.wav Look!
\ No newline at end of file
diff --git a/tools/install_faster-whisper.sh b/tools/install_faster-whisper.sh
new file mode 100644
index 0000000..45a7f81
--- /dev/null
+++ b/tools/install_faster-whisper.sh
@@ -0,0 +1,23 @@
+#!/bin/bash
+set -e
+
+pip install faster-whisper
+
+if ! command -v nvcc &>/dev/null; then
+  echo "Error: nvcc not found. Please install the CUDA Toolkit first." >&2
+  exit 1
+fi
+
+cuda_ver=$(nvcc --version | sed -nE 's/.*release ([0-9]+\.[0-9]+).*/\1/p')
+cuda_major=${cuda_ver%%.*}
+echo "Detected CUDA version:$cuda_ver"
+
+if [ "$cuda_major" -ge 12 ]; then
+  conda install -c conda-forge "cudnn=9.*" "numpy<2.3"
+elif [ "$cuda_major" -eq 11 ]; then
+  conda install -c conda-forge "cudnn=8.*" "numpy<2.3"
+  pip install --force-reinstall 'ctranslate2==3.24.0' 'numpy<2.2'
+else
+  echo "Error: Unsupported CUDA major version $cuda_major" >&2
+  exit 1
+fi
\ No newline at end of file
diff --git a/versa/__init__.py b/versa/__init__.py
index 8471d37..7f8ca42 100644
--- a/versa/__init__.py
+++ b/versa/__init__.py
@@ -59,6 +59,14 @@
     faster_whisper_levenshtein_metric,
     faster_whisper_wer_setup,
 )
+from versa.corpus_metrics.nemo_wer import (
+    nemo_levenshtein_metric,
+    nemo_wer_setup,
+)
+from versa.corpus_metrics.hubert_wer import (
+    hubert_levenshtein_metric,
+    hubert_wer_setup,
+)
 from versa.utterance_metrics.asr_matching import asr_match_metric, asr_match_setup
 from versa.utterance_metrics.audiobox_aesthetics_score import (
     audiobox_aesthetics_score,
diff --git a/versa/corpus_metrics/faster_whisper_wer.py b/versa/corpus_metrics/faster_whisper_wer.py
new file mode 100644
index 0000000..fe07a7d
--- /dev/null
+++ b/versa/corpus_metrics/faster_whisper_wer.py
@@ -0,0 +1,149 @@
+#!/usr/bin/env python3
+
+# Copyright 2025 Haoran Wang
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+import logging
+
+import librosa
+import numpy as np
+import torch
+from Levenshtein import opcodes
+
+try:
+    from faster_whisper import WhisperModel, BatchedInferencePipeline
+except ImportError:
+    logging.warning(
+        "Faster-whisper is not properly installed. Please install following https://github.com/systran/faster-whisper"
+    )
+    WhisperModel = None
+
+from espnet2.text.cleaner import TextCleaner
+
+TARGET_FS = 16000
+
+
+def faster_whisper_wer_setup(
+    model_tag="default", beam_size=5, batch_size=1, compute_type="float32" ,text_cleaner="whisper_basic", use_gpu=True
+):
+    if model_tag == "default":
+        model_tag = "large-v3"
+    device = "cuda" if use_gpu else "cpu"
+    if WhisperModel is None:
+        raise RuntimeError(
+            "Whisper WER is used for evaluation while faster-whisper is not installed"
+        )
+    model_whisper = WhisperModel(model_tag, device=device, compute_type=compute_type)
+    if batch_size > 1:
+        model = BatchedInferencePipeline(model=model_whisper)
+    else:
+        model = model_whisper
+    textcleaner = TextCleaner(text_cleaner)
+    wer_utils = {"model": model, "cleaner": textcleaner, "beam_size": beam_size, "batch_size": batch_size, "compute_type": compute_type}
+    return wer_utils
+
+
+def faster_whisper_levenshtein_metric(
+    wer_utils, pred_x, ref_text, fs=16000, cache_pred_text=None
+):
+    """Calculate the Levenshtein distance between ref and inf ASR results.
+
+    Args:
+        wer_utils (dict): a utility dict for WER calculation.
+            including: faster-whisper model ("model"), text cleaner ("textcleaner"), 
+            beam size ("beam size") and batch size ("batch_size")
+        pred_x (np.ndarray): test signal (time,)
+        ref_text (string): reference transcript
+        cache_pred_text (string): transcription from cache (previous modules)
+        fs (int): sampling rate in Hz
+    Returns:
+        ret (dict): ditionary containing occurrences of edit operations
+    """
+    if cache_pred_text is not None:
+        inf_text = cache_pred_text
+    else:
+        if fs != TARGET_FS:
+            pred_x = librosa.resample(pred_x, orig_sr=fs, target_sr=TARGET_FS)
+            fs = TARGET_FS
+        with torch.no_grad():
+            if wer_utils["batch_size"] > 1:
+                pred_x = pred_x.astype(getattr(np, wer_utils["compute_type"]))
+                inf_output, _ = wer_utils["model"].transcribe(
+                    pred_x, beam_size=wer_utils["beam_size"], batch_size=wer_utils["batch_size"]
+                )
+                inf_text = "".join(segment.text for segment in inf_output)
+            else:
+                inf_output, _ = wer_utils["model"].transcribe(
+                    pred_x, beam_size=wer_utils["beam_size"]
+                )
+                inf_text = "".join(segment.text for segment in inf_output)
+
+
+    ref_text = wer_utils["cleaner"](ref_text).strip()
+    pred_text = wer_utils["cleaner"](inf_text).strip()
+
+    # process wer
+    ref_words = ref_text.strip().split()
+    pred_words = pred_text.strip().split()
+    ret = {
+        "faster_whisper_hyp_text": pred_text,
+        "ref_text": ref_text,
+        "faster_whisper_wer_delete": 0,
+        "faster_whisper_wer_insert": 0,
+        "faster_whisper_wer_replace": 0,
+        "faster_whisper_wer_equal": 0,
+    }
+    for op, ref_st, ref_et, inf_st, inf_et in opcodes(ref_words, pred_words):
+        if op == "insert":
+            ret["faster_whisper_wer_" + op] = ret["faster_whisper_wer_" + op] + inf_et - inf_st
+        else:
+            ret["faster_whisper_wer_" + op] = ret["faster_whisper_wer_" + op] + ref_et - ref_st
+    total = (
+        ret["faster_whisper_wer_delete"]
+        + ret["faster_whisper_wer_replace"]
+        + ret["faster_whisper_wer_equal"]
+    )
+    assert total == len(ref_words), (total, len(ref_words))
+    total = (
+        ret["faster_whisper_wer_insert"]
+        + ret["faster_whisper_wer_replace"]
+        + ret["faster_whisper_wer_equal"]
+    )
+    assert total == len(pred_words), (total, len(pred_words))
+
+    # process cer
+    ref_words = [c for c in ref_text]
+    pred_words = [c for c in pred_text]
+    ret["faster_whisper_cer_delete"] = 0
+    ret["faster_whisper_cer_insert"] = 0
+    ret["faster_whisper_cer_replace"] = 0
+    ret["faster_whisper_cer_equal"] = 0
+    for op, ref_st, ref_et, inf_st, inf_et in opcodes(ref_words, pred_words):
+        if op == "insert":
+            ret["faster_whisper_cer_" + op] = ret["faster_whisper_cer_" + op] + inf_et - inf_st
+        else:
+            ret["faster_whisper_cer_" + op] = ret["faster_whisper_cer_" + op] + ref_et - ref_st
+    total = (
+        ret["faster_whisper_cer_delete"]
+        + ret["faster_whisper_cer_replace"]
+        + ret["faster_whisper_cer_equal"]
+    )
+    assert total == len(ref_words), (total, len(ref_words))
+    total = (
+        ret["faster_whisper_cer_insert"]
+        + ret["faster_whisper_cer_replace"]
+        + ret["faster_whisper_cer_equal"]
+    )
+    assert total == len(pred_words), (total, len(pred_words))
+
+    return ret
+
+
+if __name__ == "__main__":
+    a = np.random.random(16000)
+    wer_utils = faster_whisper_wer_setup()
+    print(
+        "metrics: {}".format(
+            faster_whisper_levenshtein_metric(wer_utils, a, "test a sentence.", 16000)
+        )
+    )
diff --git a/versa/corpus_metrics/hubert_wer.py b/versa/corpus_metrics/hubert_wer.py
new file mode 100644
index 0000000..efb7d5d
--- /dev/null
+++ b/versa/corpus_metrics/hubert_wer.py
@@ -0,0 +1,141 @@
+#!/usr/bin/env python3
+
+# Copyright 2024 Jiatong Shi
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+import logging
+
+import librosa
+import numpy as np
+import torch
+from Levenshtein import opcodes
+
+try:
+    from transformers import Wav2Vec2Processor, HubertForCTC
+except ImportError:
+    logging.warning(
+        "transformers is not properly installed."
+    )
+    Wav2Vec2Processor = None
+    HubertForCTC = None
+
+from espnet2.text.cleaner import TextCleaner
+
+TARGET_FS = 16000
+
+
+def hubert_wer_setup(
+    model_tag="default", text_cleaner="whisper_basic", use_gpu=True
+):
+    if model_tag == "default":
+        model_tag = "facebook/hubert-large-ls960-ft"
+    device = "cuda" if use_gpu else "cpu"
+    if Wav2Vec2Processor is None and HubertForCTC is None:
+        raise RuntimeError(
+            "Facebook's hubert WER is used for evaluation while transformers is not installed"
+        )
+    processor = Wav2Vec2Processor.from_pretrained(model_tag)
+    model = HubertForCTC.from_pretrained(model_tag)
+
+    textcleaner = TextCleaner(text_cleaner)
+    wer_utils = {"model": model, "processor": processor, "cleaner": textcleaner}
+    return wer_utils
+
+
+def hubert_levenshtein_metric(
+    wer_utils, pred_x, ref_text, fs=16000, cache_pred_text=None
+):
+    """Calculate the Levenshtein distance between ref and inf ASR results.
+
+    Args:
+        wer_utils (dict): a utility dict for WER calculation.
+            including: hubert asr model ("model"), text cleaner ("textcleaner")
+        pred_x (np.ndarray): test signal (time,)
+        ref_text (string): reference transcript
+        cache_pred_text (string): transcription from cache (previous modules)
+        fs (int): sampling rate in Hz
+    Returns:
+        ret (dict): ditionary containing occurrences of edit operations
+    """
+    if cache_pred_text is not None:
+        inf_text = cache_pred_text
+    else:
+        if fs != TARGET_FS:
+            pred_x = librosa.resample(pred_x, orig_sr=fs, target_sr=TARGET_FS)
+            fs = TARGET_FS
+        with torch.no_grad():
+            input_values = wer_utils["processor"](pred_x, return_tensors="pt").input_values
+            logits = wer_utils["model"](input_values).logits
+            predicted_ids = torch.argmax(logits, dim=-1)
+            inf_text = wer_utils["processor"].decode(predicted_ids[0])
+
+    ref_text = wer_utils["cleaner"](ref_text).strip()
+    pred_text = wer_utils["cleaner"](inf_text).strip()
+
+    # process wer
+    ref_words = ref_text.strip().split()
+    pred_words = pred_text.strip().split()
+    ret = {
+        "hubert_hyp_text": pred_text,
+        "ref_text": ref_text,
+        "hubert_wer_delete": 0,
+        "hubert_wer_insert": 0,
+        "hubert_wer_replace": 0,
+        "hubert_wer_equal": 0,
+    }
+    for op, ref_st, ref_et, inf_st, inf_et in opcodes(ref_words, pred_words):
+        if op == "insert":
+            ret["hubert_wer_" + op] = ret["hubert_wer_" + op] + inf_et - inf_st
+        else:
+            ret["hubert_wer_" + op] = ret["hubert_wer_" + op] + ref_et - ref_st
+    total = (
+        ret["hubert_wer_delete"]
+        + ret["hubert_wer_replace"]
+        + ret["hubert_wer_equal"]
+    )
+    assert total == len(ref_words), (total, len(ref_words))
+    total = (
+        ret["hubert_wer_insert"]
+        + ret["hubert_wer_replace"]
+        + ret["hubert_wer_equal"]
+    )
+    assert total == len(pred_words), (total, len(pred_words))
+
+    # process cer
+    ref_words = [c for c in ref_text]
+    pred_words = [c for c in pred_text]
+    ret.update(
+        hubert_cer_delete=0,
+        hubert_cer_insert=0,
+        hubert_cer_replace=0,
+        hubert_cer_equal=0,
+    )
+    for op, ref_st, ref_et, inf_st, inf_et in opcodes(ref_words, pred_words):
+        if op == "insert":
+            ret["hubert_cer_" + op] = ret["hubert_cer_" + op] + inf_et - inf_st
+        else:
+            ret["hubert_cer_" + op] = ret["hubert_cer_" + op] + ref_et - ref_st
+    total = (
+        ret["hubert_cer_delete"]
+        + ret["hubert_cer_replace"]
+        + ret["hubert_cer_equal"]
+    )
+    assert total == len(ref_words), (total, len(ref_words))
+    total = (
+        ret["hubert_cer_insert"]
+        + ret["hubert_cer_replace"]
+        + ret["hubert_cer_equal"]
+    )
+    assert total == len(pred_words), (total, len(pred_words))
+
+    return ret
+
+
+if __name__ == "__main__":
+    a = np.random.random(16000)
+    wer_utils = hubert_wer_setup()
+    print(
+        "metrics: {}".format(
+            hubert_levenshtein_metric(wer_utils, a, "test a sentence.", 16000)
+        )
+    )
diff --git a/versa/corpus_metrics/nemo_wer.py b/versa/corpus_metrics/nemo_wer.py
new file mode 100644
index 0000000..16277cf
--- /dev/null
+++ b/versa/corpus_metrics/nemo_wer.py
@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+
+# Copyright 2024 Jiatong Shi
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+import logging
+
+import librosa
+import numpy as np
+import torch
+from Levenshtein import opcodes
+
+try:
+    import nemo.collections.asr as nemo_asr
+except ImportError:
+    logging.warning(
+        "NeMo is not properly installed. Please install following https://github.com/NVIDIA/NeMo"
+    )
+    nemo_asr= None
+
+from espnet2.text.cleaner import TextCleaner
+
+TARGET_FS = 16000
+
+
+def nemo_wer_setup(
+    model_tag="default", text_cleaner="whisper_basic", use_gpu=True
+):
+    if model_tag == "default":
+        model_tag = "nvidia/stt_en_conformer_transducer_xlarge"
+    device = "cuda" if use_gpu else "cpu"
+    if nemo_asr is None:
+        raise RuntimeError(
+            "NeMo WER is used for evaluation while NeMo is not installed"
+        )
+    asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_tag)
+    textcleaner = TextCleaner(text_cleaner)
+    wer_utils = {"model": asr_model, "cleaner": textcleaner}
+    return wer_utils
+
+
+def nemo_levenshtein_metric(
+    wer_utils, pred_x, ref_text, fs=16000, cache_pred_text=None
+):
+    """Calculate the Levenshtein distance between ref and inf ASR results.
+
+    Args:
+        wer_utils (dict): a utility dict for WER calculation.
+            including: nemo asr model ("model"), text cleaner ("textcleaner")
+        pred_x (np.ndarray): test signal (time,)
+        ref_text (string): reference transcript
+        cache_pred_text (string): transcription from cache (previous modules)
+        fs (int): sampling rate in Hz
+    Returns:
+        ret (dict): ditionary containing occurrences of edit operations
+    """
+    if cache_pred_text is not None:
+        inf_text = cache_pred_text
+    else:
+        if fs != TARGET_FS:
+            pred_x = librosa.resample(pred_x, orig_sr=fs, target_sr=TARGET_FS)
+            fs = TARGET_FS
+        with torch.no_grad():
+            inf_text = wer_utils["model"].transcribe(
+                audio=pred_x
+            )[0].text
+
+    ref_text = wer_utils["cleaner"](ref_text).strip()
+    pred_text = wer_utils["cleaner"](inf_text).strip()
+
+    # process wer
+    ref_words = ref_text.strip().split()
+    pred_words = pred_text.strip().split()
+    ret = {
+        "nemo_hyp_text": pred_text,
+        "ref_text": ref_text,
+        "nemo_wer_delete": 0,
+        "nemo_wer_insert": 0,
+        "nemo_wer_replace": 0,
+        "nemo_wer_equal": 0,
+    }
+    for op, ref_st, ref_et, inf_st, inf_et in opcodes(ref_words, pred_words):
+        if op == "insert":
+            ret["nemo_wer_" + op] = ret["nemo_wer_" + op] + inf_et - inf_st
+        else:
+            ret["nemo_wer_" + op] = ret["nemo_wer_" + op] + ref_et - ref_st
+    total = (
+        ret["nemo_wer_delete"]
+        + ret["nemo_wer_replace"]
+        + ret["nemo_wer_equal"]
+    )
+    assert total == len(ref_words), (total, len(ref_words))
+    total = (
+        ret["nemo_wer_insert"]
+        + ret["nemo_wer_replace"]
+        + ret["nemo_wer_equal"]
+    )
+    assert total == len(pred_words), (total, len(pred_words))
+
+    # process cer
+    ref_words = [c for c in ref_text]
+    pred_words = [c for c in pred_text]
+    ret.update(
+        nemo_cer_delete=0,
+        nemo_cer_insert=0,
+        nemo_cer_replace=0,
+        nemo_cer_equal=0,
+    )
+    for op, ref_st, ref_et, inf_st, inf_et in opcodes(ref_words, pred_words):
+        if op == "insert":
+            ret["nemo_cer_" + op] = ret["nemo_cer_" + op] + inf_et - inf_st
+        else:
+            ret["nemo_cer_" + op] = ret["nemo_cer_" + op] + ref_et - ref_st
+    total = (
+        ret["nemo_cer_delete"]
+        + ret["nemo_cer_replace"]
+        + ret["nemo_cer_equal"]
+    )
+    assert total == len(ref_words), (total, len(ref_words))
+    total = (
+        ret["nemo_cer_insert"]
+        + ret["nemo_cer_replace"]
+        + ret["nemo_cer_equal"]
+    )
+    assert total == len(pred_words), (total, len(pred_words))
+
+    return ret
+
+
+if __name__ == "__main__":
+    a = np.random.random(16000)
+    wer_utils = nemo_wer_setup()
+    print(
+        "metrics: {}".format(
+            nemo_levenshtein_metric(wer_utils, a, "test a sentence.", 16000)
+        )
+    )
diff --git a/versa/metrics.py b/versa/metrics.py
index 067b6d2..e4c5029 100644
--- a/versa/metrics.py
+++ b/versa/metrics.py
@@ -31,6 +31,9 @@
     "espnet_hyp_text",
     "owsm_hyp_text",
     "whisper_hyp_text",
+    "faster_whisper_hyp_text",
+    "nemo_hyp_text",
+    "hubert_hyp_text"
 ]
 
 NUM_METRIC = [
@@ -112,14 +115,44 @@
     "owsm_cer_equal",
     "whisper_wer",
     "whisper_wer_delete",
-    "espnet_wer_insert",
-    "espnet_wer_replace",
-    "espnet_wer_equal",
+    "whisper_wer_insert",
+    "whisper_wer_replace",
+    "whisper_wer_equal",
     "whisper_cer",
     "whisper_cer_delete",
-    "espnet_cer_insert",
-    "espnet_cer_replace",
-    "espnet_cer_equal",
+    "whisper_cer_insert",
+    "whisper_cer_replace",
+    "whisper_cer_equal",
+    "faster_whisper_wer",
+    "faster_whisper_wer_delete",
+    "faster_whisper_wer_insert",
+    "faster_whisper_wer_replace",
+    "faster_whisper_wer_equal",
+    "faster_whisper_cer",
+    "faster_whisper_cer_delete",
+    "faster_whisper_cer_insert",
+    "faster_whisper_cer_replace",
+    "faster_whisper_cer_equal",
+    "nemo_wer",
+    "nemo_wer_delete",
+    "nemo_wer_insert",
+    "nemo_wer_replace",
+    "nemo_wer_equal",
+    "nemo_cer",
+    "nemo_cer_delete",
+    "nemo_cer_insert",
+    "nemo_cer_replace",
+    "nemo_cer_equal",
+    "hubert_wer",
+    "hubert_wer_delete",
+    "hubert_wer_insert",
+    "hubert_wer_replace",
+    "hubert_wer_equal",
+    "hubert_cer",
+    "hubert_cer_delete",
+    "hubert_cer_insert",
+    "hubert_cer_replace",
+    "hubert_cer_equal",
     "emotion_similarity",
     "spk_similarity",
     "nomad",
diff --git a/versa/scorer_shared.py b/versa/scorer_shared.py
index f9d35e7..fcafff7 100644
--- a/versa/scorer_shared.py
+++ b/versa/scorer_shared.py
@@ -390,6 +390,58 @@ def load_score_modules(score_config, use_gt=True, use_gt_text=False, use_gpu=Fal
                 "args": args_cache,
             }
             logging.info("Initiate faster_whisper WER calculation successfully")
+        elif config["name"] == "nemo_wer":
+            if not use_gt_text:
+                logging.warning("Cannot use nemo_wer because no gt text is provided")
+                continue
+
+            logging.info("Loading nemo_wer metric with reference text")
+            from versa import nemo_levenshtein_metric, nemo_wer_setup
+
+            # Load nemo asr model if it is already loaded
+            if (
+                "speaking_rate" in score_modules.keys()
+                or "asr_matching" in score_modules.keys()
+            ):
+                args_cache = score_modules["speaking_rate"]["args"]
+            else:
+                args_cache = nemo_wer_setup(
+                    model_tag=config.get("model_tag", "default"),
+                    text_cleaner=config.get("text_cleaner", "whisper_basic"),
+                    use_gpu=use_gpu,
+                )
+
+            score_modules["nemo_wer"] = {
+                "module": nemo_levenshtein_metric,
+                "args": args_cache,
+            }
+            logging.info("Initiate NeMo WER calculation successfully")
+        elif config["name"] == "hubert_wer":
+            if not use_gt_text:
+                logging.warning("Cannot use hubert_wer because no gt text is provided")
+                continue
+
+            logging.info("Loading hubert_wer metric with reference text")
+            from versa import hubert_levenshtein_metric, hubert_wer_setup
+
+            # Load hubert asr model if it is already loaded
+            if (
+                "speaking_rate" in score_modules.keys()
+                or "asr_matching" in score_modules.keys()
+            ):
+                args_cache = score_modules["speaking_rate"]["args"]
+            else:
+                args_cache = hubert_wer_setup(
+                    model_tag=config.get("model_tag", "default"),
+                    text_cleaner=config.get("text_cleaner", "whisper_basic"),
+                    use_gpu=use_gpu,
+                )
+
+            score_modules["hubert_wer"] = {
+                "module": hubert_levenshtein_metric,
+                "args": args_cache,
+            }
+            logging.info("Initiate hubert WER calculation successfully")
         elif config["name"] == "scoreq_ref":
             if not use_gt:
                 logging.warning("Cannot use scoreq_ref because no gt audio is provided")
@@ -1029,7 +1081,7 @@ def use_score_modules(score_modules, gen_wav, gt_wav, gen_sr, text=None):
             score = score_modules[key]["module"](
                 score_modules[key]["model"], gen_wav, gt_wav, gen_sr
             )
-        elif key == "espnet_wer" or key == "owsm_wer" or key == "whisper_wer" or key == "faster_whisper_wer":
+        elif key in ["espnet_wer", "owsm_wer", "whisper_wer", "faster_whisper_wer", "nemo_wer", "hubert_wer"]:
             score = score_modules[key]["module"](
                 score_modules[key]["args"],
                 gen_wav,

From 50d10c5b8593501096562c5a32d27975b7433a4d Mon Sep 17 00:00:00 2001
From: Haoran Wang <2472356642@qq.com>
Date: Mon, 23 Jun 2025 13:21:26 -0500
Subject: [PATCH 3/6] fix: copyright

---
 versa/corpus_metrics/hubert_wer.py | 2 +-
 versa/corpus_metrics/nemo_wer.py   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/versa/corpus_metrics/hubert_wer.py b/versa/corpus_metrics/hubert_wer.py
index efb7d5d..ffcd249 100644
--- a/versa/corpus_metrics/hubert_wer.py
+++ b/versa/corpus_metrics/hubert_wer.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 
-# Copyright 2024 Jiatong Shi
+# Copyright 2025 Haoran Wang
 #  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
 
 import logging
diff --git a/versa/corpus_metrics/nemo_wer.py b/versa/corpus_metrics/nemo_wer.py
index 16277cf..57d4c5b 100644
--- a/versa/corpus_metrics/nemo_wer.py
+++ b/versa/corpus_metrics/nemo_wer.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 
-# Copyright 2024 Jiatong Shi
+# Copyright 2025 Haoran Wang
 #  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
 
 import logging

From 718b2ed688fc3fdbb6ee40da3c84b373abd0d4ed Mon Sep 17 00:00:00 2001
From: Haoran Wang <2472356642@qq.com>
Date: Mon, 23 Jun 2025 13:27:55 -0500
Subject: [PATCH 4/6] fix: a small bug in scorer_shared/list_scoring

---
 versa/scorer_shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/versa/scorer_shared.py b/versa/scorer_shared.py
index fcafff7..4b768b0 100644
--- a/versa/scorer_shared.py
+++ b/versa/scorer_shared.py
@@ -1223,7 +1223,7 @@ def list_scoring(
 
         # Step2: load reference (gt) speech and conduct basic checks
         if gt_files is not None:
-            if key not in gen_files.keys():
+            if key not in gt_files.keys():
                 logging.warning(
                     "key {} not found in ground truth files though provided, skipping".format(
                         key

From c2892433c768afb283e0603e9a881c529bd1e2df Mon Sep 17 00:00:00 2001
From: Haoran Wang <2472356642@qq.com>
Date: Mon, 23 Jun 2025 13:53:37 -0500
Subject: [PATCH 5/6] add: docs

---
 docs/supported_metrics.md | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/docs/supported_metrics.md b/docs/supported_metrics.md
index d72d1a2..14023e1 100644
--- a/docs/supported_metrics.md
+++ b/docs/supported_metrics.md
@@ -101,14 +101,17 @@ We include x mark if the metric is auto-installed in versa.
 | 3 | x | ESPnet Speech Recognition-based Error Rate | espnet_wer | espnet_wer |[ESPnet](https://github.com/espnet/espnet) | [paper](https://arxiv.org/pdf/1804.00015) |
 | 4 | x | ESPnet-OWSM Speech Recognition-based Error Rate | owsm_wer | owsm_wer |[ESPnet](https://github.com/espnet/espnet) | [paper](https://arxiv.org/abs/2309.13876) |
 | 5 | x | OpenAI-Whisper Speech Recognition-based Error Rate | whisper_wer | whisper_wer |[Whisper](https://github.com/openai/whisper) | [paper](https://arxiv.org/abs/2212.04356) |
-| 6 |   | Emotion2vec similarity (emo2vec) | emo2vec_similarity | emotion_similarity | [emo2vec](https://github.com/ftshijt/emotion2vec/tree/main) | [paper](https://arxiv.org/abs/2312.15185) | 
-| 7 | x | Speaker Embedding Similarity  | speaker | spk_similarity | [espnet](https://github.com/espnet/espnet) | [paper](https://arxiv.org/abs/2401.17230) |
-| 8 |   | NOMAD: Unsupervised Learning of Perceptual Embeddings For Speech Enhancement and Non-Matching Reference Audio Quality Assessment |  nomad | nomad |[Nomad](https://github.com/shimhz/nomad/tree/main) | [paper](https://arxiv.org/abs/2309.16284) |
-| 9 |   | Contrastive Language-Audio Pretraining Score (CLAP Score) | clap_score | clap_score | [fadtk](https://github.com/gudgud96/frechet-audio-distance) | [paper](https://arxiv.org/abs/2301.12661) |
-| 10 |   | Accompaniment Prompt Adherence (APA) | apa | apa | [Sony-audio-metrics](https://github.com/SonyCSLParis/audio-metrics) | [paper](https://arxiv.org/abs/2404.00775) |
-| 11 |  | Log Likelihood Ratio (LLR) | pysepm | pysepm_llr | [pysepm](https://github.com/shimhz/pysepm.git) | [Paper](https://ecs.utdallas.edu/loizou/speech/obj_paper_jan08.pdf)|
-| 12 | x | Uni-VERSA (Versatile Speech Assessment with a Unified Framework) with Paired Text | universa | universa_{sub_metrics} | [Uni-VERSA](https://huggingface.co/collections/espnet/universa-6834e7c0a28225bffb6e2526) | [paper](https://arxiv.org/abs/2505.20741) |
-| 13 |  | Singer Embedding Similarity  | singer | singer_similarity | [SSL-Singer-Identity](https://github.com/SonyCSLParis/ssl-singer-identity) | [paper](https://hal.science/hal-04186048v1) |
+| 6 |   | Faster-Whisper Speech Recognition-based Error Rate | faster_whisper_wer | faster_whisper_wer |[Faster-Whisper](https://github.com/systran/faster-whisper) | - |
+| 7 | x | NVIDIA Conformer-Transducer X-Large Speech Recognition-based Error Rate | nemo_wer | nemo_wer |[NeMo](https://github.com/NVIDIA/NeMo) | [paper](https://arxiv.org/abs/2005.08100) |
+| 8 | x | Facebook Hubert-Large-Finetuned Speech Recognition-based Error Rate | hubert_wer | hubert_wer |[HuBERT](https://github.com/facebookresearch/fairseq/tree/main/examples/hubert) | [paper](https://arxiv.org/abs/2106.07447) |
+| 9 |   | Emotion2vec similarity (emo2vec) | emo2vec_similarity | emotion_similarity | [emo2vec](https://github.com/ftshijt/emotion2vec/tree/main) | [paper](https://arxiv.org/abs/2312.15185) | 
+| 10 | x | Speaker Embedding Similarity  | speaker | spk_similarity | [espnet](https://github.com/espnet/espnet) | [paper](https://arxiv.org/abs/2401.17230) |
+| 11 |   | NOMAD: Unsupervised Learning of Perceptual Embeddings For Speech Enhancement and Non-Matching Reference Audio Quality Assessment |  nomad | nomad |[Nomad](https://github.com/shimhz/nomad/tree/main) | [paper](https://arxiv.org/abs/2309.16284) |
+| 12 |   | Contrastive Language-Audio Pretraining Score (CLAP Score) | clap_score | clap_score | [fadtk](https://github.com/gudgud96/frechet-audio-distance) | [paper](https://arxiv.org/abs/2301.12661) |
+| 13 |   | Accompaniment Prompt Adherence (APA) | apa | apa | [Sony-audio-metrics](https://github.com/SonyCSLParis/audio-metrics) | [paper](https://arxiv.org/abs/2404.00775) |
+| 14 |  | Log Likelihood Ratio (LLR) | pysepm | pysepm_llr | [pysepm](https://github.com/shimhz/pysepm.git) | [Paper](https://ecs.utdallas.edu/loizou/speech/obj_paper_jan08.pdf)|
+| 15 | x | Uni-VERSA (Versatile Speech Assessment with a Unified Framework) with Paired Text | universa | universa_{sub_metrics} | [Uni-VERSA](https://huggingface.co/collections/espnet/universa-6834e7c0a28225bffb6e2526) | [paper](https://arxiv.org/abs/2505.20741) |
+| 16 |  | Singer Embedding Similarity  | singer | singer_similarity | [SSL-Singer-Identity](https://github.com/SonyCSLParis/ssl-singer-identity) | [paper](https://hal.science/hal-04186048v1) |
 
 ### Distributional Metrics (in verifying)
 

From 878ef17cf133e1888748f8f55a78158289168196 Mon Sep 17 00:00:00 2001
From: Haoran Wang <2472356642@qq.com>
Date: Tue, 24 Jun 2025 13:16:16 -0500
Subject: [PATCH 6/6] update

---
 docs/supported_metrics.md                     |  4 +-
 egs/separate_metrics/wer.yaml                 | 23 ++++---
 setup.py                                      |  2 -
 test/test_pipeline/test_general.py            |  2 +
 test/test_pipeline/test_wer.py                |  2 +-
 ..._faster-whisper.sh => install_fwhisper.sh} |  1 +
 tools/install_nemo.sh                         |  5 ++
 versa/__init__.py                             |  6 +-
 ...{faster_whisper_wer.py => fwhisper_wer.py} | 58 ++++++++---------
 versa/metrics.py                              | 22 +++----
 versa/scorer_shared.py                        | 63 ++++++-------------
 11 files changed, 85 insertions(+), 103 deletions(-)
 rename tools/{install_faster-whisper.sh => install_fwhisper.sh} (99%)
 create mode 100644 tools/install_nemo.sh
 rename versa/corpus_metrics/{faster_whisper_wer.py => fwhisper_wer.py} (72%)

diff --git a/docs/supported_metrics.md b/docs/supported_metrics.md
index 14023e1..77ce6e5 100644
--- a/docs/supported_metrics.md
+++ b/docs/supported_metrics.md
@@ -101,8 +101,8 @@ We include x mark if the metric is auto-installed in versa.
 | 3 | x | ESPnet Speech Recognition-based Error Rate | espnet_wer | espnet_wer |[ESPnet](https://github.com/espnet/espnet) | [paper](https://arxiv.org/pdf/1804.00015) |
 | 4 | x | ESPnet-OWSM Speech Recognition-based Error Rate | owsm_wer | owsm_wer |[ESPnet](https://github.com/espnet/espnet) | [paper](https://arxiv.org/abs/2309.13876) |
 | 5 | x | OpenAI-Whisper Speech Recognition-based Error Rate | whisper_wer | whisper_wer |[Whisper](https://github.com/openai/whisper) | [paper](https://arxiv.org/abs/2212.04356) |
-| 6 |   | Faster-Whisper Speech Recognition-based Error Rate | faster_whisper_wer | faster_whisper_wer |[Faster-Whisper](https://github.com/systran/faster-whisper) | - |
-| 7 | x | NVIDIA Conformer-Transducer X-Large Speech Recognition-based Error Rate | nemo_wer | nemo_wer |[NeMo](https://github.com/NVIDIA/NeMo) | [paper](https://arxiv.org/abs/2005.08100) |
+| 6 |   | Faster-Whisper Speech Recognition-based Error Rate | fwhisper_wer | fwhisper_wer |[Faster-Whisper](https://github.com/systran/faster-whisper) | - |
+| 7 |   | NVIDIA Conformer-Transducer X-Large Speech Recognition-based Error Rate | nemo_wer | nemo_wer |[NeMo](https://github.com/NVIDIA/NeMo) | [paper](https://arxiv.org/abs/2005.08100) |
 | 8 | x | Facebook Hubert-Large-Finetuned Speech Recognition-based Error Rate | hubert_wer | hubert_wer |[HuBERT](https://github.com/facebookresearch/fairseq/tree/main/examples/hubert) | [paper](https://arxiv.org/abs/2106.07447) |
 | 9 |   | Emotion2vec similarity (emo2vec) | emo2vec_similarity | emotion_similarity | [emo2vec](https://github.com/ftshijt/emotion2vec/tree/main) | [paper](https://arxiv.org/abs/2312.15185) | 
 | 10 | x | Speaker Embedding Similarity  | speaker | spk_similarity | [espnet](https://github.com/espnet/espnet) | [paper](https://arxiv.org/abs/2401.17230) |
diff --git a/egs/separate_metrics/wer.yaml b/egs/separate_metrics/wer.yaml
index d636450..1c63624 100644
--- a/egs/separate_metrics/wer.yaml
+++ b/egs/separate_metrics/wer.yaml
@@ -58,26 +58,29 @@
 
 
 # Word error rate with faster-whisper model
+# Please refer to tools/install_fwhisper.sh for installing.
 # More model_tag can be from https://github.com/SYSTRAN/faster-whisper/blob/master/faster_whisper/utils.py .
 # The default model is `large-v3`.
-# --faster_whisper_hyp_text: the hypothesis from faster-whisper ASR decoding
+# --fwhisper_hyp_text: the hypothesis from faster-whisper ASR decoding
 # --ref_text: reference text (after cleaner)
-# --faster_whisper_wer_delete: delete errors
-# --faster_whisper_wer_insert: insertion errors
-# --faster_whisper_wer_replace: replacement errors
-# --faster_whisper_wer_equal: correct matching words/character counts
-# --faster_whisper_cer_delete: delete errors
-# --faster_whisper_cer_insert: insertion errors
-# --faster_whisper_cer_replace: replacement errors
-# --faster_whisper_cer_equal: correct matching words/character counts
-- name: faster_whisper_wer
+# --fwhisper_wer_delete: delete errors
+# --fwhisper_wer_insert: insertion errors
+# --fwhisper_wer_replace: replacement errors
+# --fwhisper_wer_equal: correct matching words/character counts
+# --fwhisper_cer_delete: delete errors
+# --fwhisper_cer_insert: insertion errors
+# --fwhisper_cer_replace: replacement errors
+# --fwhisper_cer_equal: correct matching words/character counts
+- name: fwhisper_wer
   model_tag: default
   beam_size: 5
   batch_size: 1
   compute_type: float32
   text_cleaner: whisper_basic
 
+
 # Word error rate with NeMo asr model
+# Please refer to tools/install_nemo.sh for installing.
 # The default model is `nvidia/stt_en_conformer_transducer_xlarge`.
 # --nemo_hyp_text: the hypothesis from NeMo ASR decoding
 # --ref_text: reference text (after cleaner)
diff --git a/setup.py b/setup.py
index 73a719c..92a2c68 100644
--- a/setup.py
+++ b/setup.py
@@ -22,7 +22,6 @@
         "importlib-metadata",
         "kaggle",
         "kaldiio",
-        "jamo",
         "lazy_loader",
         "Levenshtein",
         "librosa",
@@ -59,7 +58,6 @@
         "espnet_model_zoo",
         "discrete-speech-metrics @ git+https://github.com/ftshijt/DiscreteSpeechMetrics.git@v1.0.2",
         "cdpam",
-        "nemo_toolkit[asr]"
     ],
     extras_require={
         "dev": [
diff --git a/test/test_pipeline/test_general.py b/test/test_pipeline/test_general.py
index 09a165b..5d55bc4 100755
--- a/test/test_pipeline/test_general.py
+++ b/test/test_pipeline/test_general.py
@@ -40,6 +40,8 @@
     "torch_squim_stoi": 0.6027805209159851,
     "torch_squim_pesq": 1.1683127880096436,
     "torch_squim_si_sdr": -11.109052658081055,
+    "dpam_distance": 0.15004253387451172,
+    "cdpam_distance": 0.05146043747663498,
     "dnsmos_pro_bvcc": 1.1717286109924316,
     "dnsmos_pro_nisqa": 1.4733699560165405,
     "dnsmos_pro_vcc2018": 1.930935263633728,
diff --git a/test/test_pipeline/test_wer.py b/test/test_pipeline/test_wer.py
index 2d14821..f358e4f 100755
--- a/test/test_pipeline/test_wer.py
+++ b/test/test_pipeline/test_wer.py
@@ -15,7 +15,7 @@
     "espnet_wer_equal": 1,
     "owsm_wer_equal": 1,
     "whisper_wer_equal": 1,
-    "faster_whisper_wer_equal": 1,
+    "fwhisper_wer_equal": 1,
     "nemo_wer_equal": 1,
     "hubert_wer_equal": 1,
 }
diff --git a/tools/install_faster-whisper.sh b/tools/install_fwhisper.sh
similarity index 99%
rename from tools/install_faster-whisper.sh
rename to tools/install_fwhisper.sh
index 45a7f81..eed9d70 100644
--- a/tools/install_faster-whisper.sh
+++ b/tools/install_fwhisper.sh
@@ -1,4 +1,5 @@
 #!/bin/bash
+
 set -e
 
 pip install faster-whisper
diff --git a/tools/install_nemo.sh b/tools/install_nemo.sh
new file mode 100644
index 0000000..431df50
--- /dev/null
+++ b/tools/install_nemo.sh
@@ -0,0 +1,5 @@
+#/bin/bash
+
+# NOTE(Haoran): Toolkit for nemo_wer
+
+pip install "nemo_toolkit[asr]"
\ No newline at end of file
diff --git a/versa/__init__.py b/versa/__init__.py
index 7f8ca42..46531d2 100644
--- a/versa/__init__.py
+++ b/versa/__init__.py
@@ -55,9 +55,9 @@
     whisper_levenshtein_metric,
     whisper_wer_setup,
 )
-from versa.corpus_metrics.faster_whisper_wer import (
-    faster_whisper_levenshtein_metric,
-    faster_whisper_wer_setup,
+from versa.corpus_metrics.fwhisper_wer import (
+    fwhisper_levenshtein_metric,
+    fwhisper_wer_setup,
 )
 from versa.corpus_metrics.nemo_wer import (
     nemo_levenshtein_metric,
diff --git a/versa/corpus_metrics/faster_whisper_wer.py b/versa/corpus_metrics/fwhisper_wer.py
similarity index 72%
rename from versa/corpus_metrics/faster_whisper_wer.py
rename to versa/corpus_metrics/fwhisper_wer.py
index fe07a7d..64cc7e2 100644
--- a/versa/corpus_metrics/faster_whisper_wer.py
+++ b/versa/corpus_metrics/fwhisper_wer.py
@@ -23,7 +23,7 @@
 TARGET_FS = 16000
 
 
-def faster_whisper_wer_setup(
+def fwhisper_wer_setup(
     model_tag="default", beam_size=5, batch_size=1, compute_type="float32" ,text_cleaner="whisper_basic", use_gpu=True
 ):
     if model_tag == "default":
@@ -43,7 +43,7 @@ def faster_whisper_wer_setup(
     return wer_utils
 
 
-def faster_whisper_levenshtein_metric(
+def fwhisper_levenshtein_metric(
     wer_utils, pred_x, ref_text, fs=16000, cache_pred_text=None
 ):
     """Calculate the Levenshtein distance between ref and inf ASR results.
@@ -86,53 +86,53 @@ def faster_whisper_levenshtein_metric(
     ref_words = ref_text.strip().split()
     pred_words = pred_text.strip().split()
     ret = {
-        "faster_whisper_hyp_text": pred_text,
+        "fwhisper_hyp_text": pred_text,
         "ref_text": ref_text,
-        "faster_whisper_wer_delete": 0,
-        "faster_whisper_wer_insert": 0,
-        "faster_whisper_wer_replace": 0,
-        "faster_whisper_wer_equal": 0,
+        "fwhisper_wer_delete": 0,
+        "fwhisper_wer_insert": 0,
+        "fwhisper_wer_replace": 0,
+        "fwhisper_wer_equal": 0,
     }
     for op, ref_st, ref_et, inf_st, inf_et in opcodes(ref_words, pred_words):
         if op == "insert":
-            ret["faster_whisper_wer_" + op] = ret["faster_whisper_wer_" + op] + inf_et - inf_st
+            ret["fwhisper_wer_" + op] = ret["fwhisper_wer_" + op] + inf_et - inf_st
         else:
-            ret["faster_whisper_wer_" + op] = ret["faster_whisper_wer_" + op] + ref_et - ref_st
+            ret["fwhisper_wer_" + op] = ret["fwhisper_wer_" + op] + ref_et - ref_st
     total = (
-        ret["faster_whisper_wer_delete"]
-        + ret["faster_whisper_wer_replace"]
-        + ret["faster_whisper_wer_equal"]
+        ret["fwhisper_wer_delete"]
+        + ret["fwhisper_wer_replace"]
+        + ret["fwhisper_wer_equal"]
     )
     assert total == len(ref_words), (total, len(ref_words))
     total = (
-        ret["faster_whisper_wer_insert"]
-        + ret["faster_whisper_wer_replace"]
-        + ret["faster_whisper_wer_equal"]
+        ret["fwhisper_wer_insert"]
+        + ret["fwhisper_wer_replace"]
+        + ret["fwhisper_wer_equal"]
     )
     assert total == len(pred_words), (total, len(pred_words))
 
     # process cer
     ref_words = [c for c in ref_text]
     pred_words = [c for c in pred_text]
-    ret["faster_whisper_cer_delete"] = 0
-    ret["faster_whisper_cer_insert"] = 0
-    ret["faster_whisper_cer_replace"] = 0
-    ret["faster_whisper_cer_equal"] = 0
+    ret["fwhisper_cer_delete"] = 0
+    ret["fwhisper_cer_insert"] = 0
+    ret["fwhisper_cer_replace"] = 0
+    ret["fwhisper_cer_equal"] = 0
     for op, ref_st, ref_et, inf_st, inf_et in opcodes(ref_words, pred_words):
         if op == "insert":
-            ret["faster_whisper_cer_" + op] = ret["faster_whisper_cer_" + op] + inf_et - inf_st
+            ret["fwhisper_cer_" + op] = ret["fwhisper_cer_" + op] + inf_et - inf_st
         else:
-            ret["faster_whisper_cer_" + op] = ret["faster_whisper_cer_" + op] + ref_et - ref_st
+            ret["fwhisper_cer_" + op] = ret["fwhisper_cer_" + op] + ref_et - ref_st
     total = (
-        ret["faster_whisper_cer_delete"]
-        + ret["faster_whisper_cer_replace"]
-        + ret["faster_whisper_cer_equal"]
+        ret["fwhisper_cer_delete"]
+        + ret["fwhisper_cer_replace"]
+        + ret["fwhisper_cer_equal"]
     )
     assert total == len(ref_words), (total, len(ref_words))
     total = (
-        ret["faster_whisper_cer_insert"]
-        + ret["faster_whisper_cer_replace"]
-        + ret["faster_whisper_cer_equal"]
+        ret["fwhisper_cer_insert"]
+        + ret["fwhisper_cer_replace"]
+        + ret["fwhisper_cer_equal"]
     )
     assert total == len(pred_words), (total, len(pred_words))
 
@@ -141,9 +141,9 @@ def faster_whisper_levenshtein_metric(
 
 if __name__ == "__main__":
     a = np.random.random(16000)
-    wer_utils = faster_whisper_wer_setup()
+    wer_utils = fwhisper_wer_setup()
     print(
         "metrics: {}".format(
-            faster_whisper_levenshtein_metric(wer_utils, a, "test a sentence.", 16000)
+            fwhisper_levenshtein_metric(wer_utils, a, "test a sentence.", 16000)
         )
     )
diff --git a/versa/metrics.py b/versa/metrics.py
index e4c5029..dd23572 100644
--- a/versa/metrics.py
+++ b/versa/metrics.py
@@ -31,7 +31,7 @@
     "espnet_hyp_text",
     "owsm_hyp_text",
     "whisper_hyp_text",
-    "faster_whisper_hyp_text",
+    "fwhisper_hyp_text",
     "nemo_hyp_text",
     "hubert_hyp_text"
 ]
@@ -123,16 +123,16 @@
     "whisper_cer_insert",
     "whisper_cer_replace",
     "whisper_cer_equal",
-    "faster_whisper_wer",
-    "faster_whisper_wer_delete",
-    "faster_whisper_wer_insert",
-    "faster_whisper_wer_replace",
-    "faster_whisper_wer_equal",
-    "faster_whisper_cer",
-    "faster_whisper_cer_delete",
-    "faster_whisper_cer_insert",
-    "faster_whisper_cer_replace",
-    "faster_whisper_cer_equal",
+    "fwhisper_wer",
+    "fwhisper_wer_delete",
+    "fwhisper_wer_insert",
+    "fwhisper_wer_replace",
+    "fwhisper_wer_equal",
+    "fwhisper_cer",
+    "fwhisper_cer_delete",
+    "fwhisper_cer_insert",
+    "fwhisper_cer_replace",
+    "fwhisper_cer_equal",
     "nemo_wer",
     "nemo_wer_delete",
     "nemo_wer_insert",
diff --git a/versa/scorer_shared.py b/versa/scorer_shared.py
index 4b768b0..c843f98 100644
--- a/versa/scorer_shared.py
+++ b/versa/scorer_shared.py
@@ -361,35 +361,26 @@ def load_score_modules(score_config, use_gt=True, use_gt_text=False, use_gpu=Fal
                 "args": args_cache,
             }
             logging.info("Initiate Whisper WER calculation successfully")
-        elif config["name"] == "faster_whisper_wer":
+        elif config["name"] == "fwhisper_wer":
             if not use_gt_text:
-                logging.warning("Cannot use faster_whisper_wer because no gt text is provided")
+                logging.warning("Cannot use fwhisper_wer because no gt text is provided")
                 continue
 
-            logging.info("Loading faster_whisper_wer metric with reference text")
-            from versa import faster_whisper_levenshtein_metric, faster_whisper_wer_setup
+            logging.info("Loading fwhisper_wer metric with reference text")
+            from versa import fwhisper_levenshtein_metric, fwhisper_wer_setup
 
-            # Load whisper model if it is already loaded
-            if (
-                "speaking_rate" in score_modules.keys()
-                or "asr_matching" in score_modules.keys()
-            ):
-                args_cache = score_modules["speaking_rate"]["args"]
-            else:
-                args_cache = faster_whisper_wer_setup(
+            score_modules["fwhisper_wer"] = {
+                "module": fwhisper_levenshtein_metric,
+                "args": fwhisper_wer_setup(
                     model_tag=config.get("model_tag", "default"),
                     beam_size=config.get("beam_size", 1),
                     batch_size=config.get("batch_size", 1),
                     compute_type=config.get("compute_type", "float32"),
                     text_cleaner=config.get("text_cleaner", "whisper_basic"),
                     use_gpu=use_gpu,
-                )
-
-            score_modules["faster_whisper_wer"] = {
-                "module": faster_whisper_levenshtein_metric,
-                "args": args_cache,
+                ),
             }
-            logging.info("Initiate faster_whisper WER calculation successfully")
+            logging.info("Initiate fwhisper WER calculation successfully")
         elif config["name"] == "nemo_wer":
             if not use_gt_text:
                 logging.warning("Cannot use nemo_wer because no gt text is provided")
@@ -398,22 +389,13 @@ def load_score_modules(score_config, use_gt=True, use_gt_text=False, use_gpu=Fal
             logging.info("Loading nemo_wer metric with reference text")
             from versa import nemo_levenshtein_metric, nemo_wer_setup
 
-            # Load nemo asr model if it is already loaded
-            if (
-                "speaking_rate" in score_modules.keys()
-                or "asr_matching" in score_modules.keys()
-            ):
-                args_cache = score_modules["speaking_rate"]["args"]
-            else:
-                args_cache = nemo_wer_setup(
+            score_modules["nemo_wer"] = {
+                "module": nemo_levenshtein_metric,
+                "args": nemo_wer_setup(
                     model_tag=config.get("model_tag", "default"),
                     text_cleaner=config.get("text_cleaner", "whisper_basic"),
                     use_gpu=use_gpu,
-                )
-
-            score_modules["nemo_wer"] = {
-                "module": nemo_levenshtein_metric,
-                "args": args_cache,
+                ),
             }
             logging.info("Initiate NeMo WER calculation successfully")
         elif config["name"] == "hubert_wer":
@@ -424,22 +406,13 @@ def load_score_modules(score_config, use_gt=True, use_gt_text=False, use_gpu=Fal
             logging.info("Loading hubert_wer metric with reference text")
             from versa import hubert_levenshtein_metric, hubert_wer_setup
 
-            # Load hubert asr model if it is already loaded
-            if (
-                "speaking_rate" in score_modules.keys()
-                or "asr_matching" in score_modules.keys()
-            ):
-                args_cache = score_modules["speaking_rate"]["args"]
-            else:
-                args_cache = hubert_wer_setup(
+            score_modules["hubert_wer"] = {
+                "module": hubert_levenshtein_metric,
+                "args": hubert_wer_setup(
                     model_tag=config.get("model_tag", "default"),
                     text_cleaner=config.get("text_cleaner", "whisper_basic"),
                     use_gpu=use_gpu,
-                )
-
-            score_modules["hubert_wer"] = {
-                "module": hubert_levenshtein_metric,
-                "args": args_cache,
+                ),
             }
             logging.info("Initiate hubert WER calculation successfully")
         elif config["name"] == "scoreq_ref":
@@ -1081,7 +1054,7 @@ def use_score_modules(score_modules, gen_wav, gt_wav, gen_sr, text=None):
             score = score_modules[key]["module"](
                 score_modules[key]["model"], gen_wav, gt_wav, gen_sr
             )
-        elif key in ["espnet_wer", "owsm_wer", "whisper_wer", "faster_whisper_wer", "nemo_wer", "hubert_wer"]:
+        elif key in ["espnet_wer", "owsm_wer", "whisper_wer", "fwhisper_wer", "nemo_wer", "hubert_wer"]:
             score = score_modules[key]["module"](
                 score_modules[key]["args"],
                 gen_wav,