Datasets:

nvidia
/

hifitts-2

@@ -76,7 +76,7 @@ The dataset contains an audiobook chapter level manifest with these fields:
 - **utterances.offset**: Offset of utterance within the chapter
 - **utterances.duration**: Duration of utterance
-Bandwidth is estimated from the first 30 seconds of each audiobook using the approach from [`estimate_bandwidth.py`](https://github.com/NVIDIA/NeMo-speech-data-processor/blob/main/sdp/processors/nemo/estimate_bandwidth.py#L65) in [Speech Data Processor (SDP) Toolkit](https://github.com/NVIDIA/NeMo-speech-data-processor). The bandwidth fmax is estimated by using the mean of the power spectrum to find the highest frequency that has at least -50 dB level
 relative to the peak value of the spectrum, namely,
 $$f_{\text{max}} = \max\left\{f \in [0, f_{\text{Nyquist}}] \, \bigg|\, 10 \log_{10} \left(\frac{P(f)}{P_{\text{peak}}}\right) \geq -50\, \text{dB}\right\}$$
 where `P(f)` is the power spectral density and `P_peak` the maximum spectral power.

 - **utterances.offset**: Offset of utterance within the chapter
 - **utterances.duration**: Duration of utterance
+Bandwidth is estimated from the first 30 seconds of each audiobook using the approach from [Speech Data Processor (SDP) Toolkit](https://github.com/NVIDIA/NeMo-speech-data-processor/blob/main/sdp/processors/modify_manifest/data_to_data.py#L1259). The bandwidth fmax is estimated by using the mean of the power spectrum to find the highest frequency that has at least -50 dB level
 relative to the peak value of the spectrum, namely,
 $$f_{\text{max}} = \max\left\{f \in [0, f_{\text{Nyquist}}] \, \bigg|\, 10 \log_{10} \left(\frac{P(f)}{P_{\text{peak}}}\right) \geq -50\, \text{dB}\right\}$$
 where `P(f)` is the power spectral density and `P_peak` the maximum spectral power.