Video reading: torchaudio.io.StreamReader seek method returns the first frame, regardless of the input start_timestep (on version 0.13.1)

[StolikTomer]

🐛 Describe the bug

Using this code:

        import torch
        from torchaudio.io import StreamReader

        video_path = <my_local_path>

        stream_reader = StreamReader(video_path)
        stream_reader.add_video_stream(5, decoder= 'h264_cuvid', hw_accel='cuda')

        start_timestep = 10
        stream_reader.seek(start_timestep)
        for (chunk, ) in stream_reader.stream():
            for frame in chunk:
                yield frame

The seek method is ignored. The resulting video iterator always starts with the 0 starting frame.

Versions

Collecting environment information...

CUDA runtime version: 11.7.99 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA A10G Nvidia driver version: 555.42.02 cuDNN version: Probably one of the following: /usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn.so.8.6.0 /usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.6.0 /usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.6.0 /usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.6.0 /usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.6.0 /usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.6.0 /usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.6.0 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True

CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 8 On-line CPU(s) list: 0-7 Vendor ID: AuthenticAMD Model name: AMD EPYC 7R32 CPU family: 23 Model: 49 Thread(s) per core: 2 Core(s) per socket: 4 Socket(s): 1 Stepping: 0 BogoMIPS: 5599.99 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save rdpid Hypervisor vendor: KVM Virtualization type: full L1d cache: 128 KiB (4 instances) L1i cache: 128 KiB (4 instances) L2 cache: 2 MiB (4 instances) L3 cache: 16 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-7 Vulnerability Gather data sampling: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Mitigation; untrained return thunk; SMT enabled with STIBP protection Vulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Versions of relevant libraries: [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.23.5 [pip3] onnx==1.16.0 [pip3] onnx-graphsurgeon==0.5.2 [pip3] pytorch-lightning==1.9.4 [pip3] pytorch3d==0.7.2 [pip3] torch==1.13.1+cu117 [pip3] torch-tensorrt==1.3.0 [pip3] torchaudio==0.13.1+cu117 [pip3] torchmetrics==1.3.2 [pip3] torchvision==0.14.1+cu117 [pip3] triton==2.3.0 [conda] numpy