Upload seamless_communication/cli/expressivity/evaluate/post_process_pauserate.py with huggingface_hub

seamless_communication/cli/expressivity/evaluate/post_process_pauserate.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# MIT_LICENSE file in the root directory of this source tree.
+
+import pandas as pd
+import csv
+import scipy
+from typing import Dict
+
+
+def get_pause(pause_data_tsv: str) -> Dict[str, float]:
+    utt_pause_align_data = pd.read_csv(
+        pause_data_tsv,
+        sep="\t",
+        quoting=csv.QUOTE_MINIMAL,
+    )
+    metrics = {}
+    pause_duration_weight = (
+        utt_pause_align_data.total_weight / utt_pause_align_data.total_weight.sum()
+    )
+    for score_name in [
+        "wmean_duration_score",
+        "wmean_alignment_score",
+        "wmean_joint_score",
+    ]:
+        metrics[score_name] = (
+            utt_pause_align_data[f"{score_name}"] * pause_duration_weight
+        ).sum()
+    return metrics
+
+
+def get_rate(target_speech_tsv: str, source_speech_tsv: str) -> float:
+    speech_unit = "syllable"
+
+    target_speech_df = pd.read_csv(
+        target_speech_tsv, sep="\t", quoting=csv.QUOTE_MINIMAL
+    ).set_index("id")
+    source_speech_df = pd.read_csv(
+        source_speech_tsv, sep="\t", quoting=csv.QUOTE_MINIMAL
+    ).set_index("id")
+
+    # using "syllable" speech unit for rate computation
+    src_speech_rate = source_speech_df[f"speech_rate_{speech_unit}"].to_numpy()
+    tgt_speech_rate = target_speech_df[f"speech_rate_{speech_unit}"].to_numpy()
+    src_tgt_spearman = scipy.stats.spearmanr(src_speech_rate, tgt_speech_rate)
+    return src_tgt_spearman.correlation  # type: ignore[no-any-return]