Ignore the NoUtterancesError when calculating pesq for a batch (#2753)

* filter nan

* use np

* fix tests

* fix dtype from object

* as func

* typing

* changelog

---------

Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>
Co-authored-by: Nicki Skafte Detlefsen <skaftenicki@gmail.com>
Co-authored-by: Jirka B <j.borovec+github@gmail.com>

(cherry picked from commit 8cf181f)

CHANGELOG.md

@@ -18,6 +18,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Fixed for Pearson changes inputs ([#2765](https://github.com/Lightning-AI/torchmetrics/pull/2765))
 
+
+- Fixed bug in `PESQ` metric where `NoUtterancesError` prevented calculating on a batch of data ([#2753](https://github.com/Lightning-AI/torchmetrics/pull/2753))
+
+
 ## [1.4.2] - 2022-09-12
 
 ### Added

src/torchmetrics/functional/audio/pesq.py

@@ -11,6 +11,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from typing import Any
+
 import numpy as np
 import torch
 from torch import Tensor
@@ -83,6 +85,11 @@ def perceptual_evaluation_speech_quality(
         )
     import pesq as pesq_backend
 
+    def _issubtype_number(x: Any) -> bool:
+        return np.issubdtype(type(x), np.number)
+
+    _filter_error_msg = np.vectorize(_issubtype_number)
+
     if fs not in (8000, 16000):
         raise ValueError(f"Expected argument `fs` to either be 8000 or 16000 but got {fs}")
     if mode not in ("wb", "nb"):
@@ -103,8 +110,8 @@ def perceptual_evaluation_speech_quality(
             pesq_val_np = np.empty(shape=(preds_np.shape[0]))
             for b in range(preds_np.shape[0]):
                 pesq_val_np[b] = pesq_backend.pesq(fs, target_np[b, :], preds_np[b, :], mode)
-        pesq_val = torch.from_numpy(pesq_val_np)
-        pesq_val = pesq_val.reshape(preds.shape[:-1])
+        pesq_val = torch.from_numpy(pesq_val_np[_filter_error_msg(pesq_val_np)].astype(np.float32))
+        pesq_val = pesq_val.reshape(len(pesq_val))
 
     if keep_same_device:
         return pesq_val.to(preds.device)